Security constraint unit commitment efficient solving method and system based on neighborhood search

Abstract

The invention discloses a security constraint unit commitment efficient solving method and system based on neighborhood search, and relates to the technical field of power system operation optimization, and the method comprises the steps: setting an initial iteration frequency, constructing a UC model without network security constraint, and obtaining an initial UC solving result; according to a UC model solving result, safety checking is carried out on the power transmission line, and a specific unit set is screened; building a neighborhood search cutting plane based on a specific unit set, adding the neighborhood search cutting plane into the UC model, and solving the UC model again; and after the UC model is solved again, iterative circulation is carried out, and the unit operation state and the planned output are output. According to the method, the search space of the mixed integer programming problem is effectively reduced, the invalid search range is reduced, the solving speed of the security constraint unit commitment problem is increased, the strict requirement of real-time scheduling of a power system on the solving time can be met, and the method is suitable for large-scale popularization and application. The unit combination scheme obtained through efficient solution can realize optimization of the system operation cost, and the economic benefit of the power system is improved.

Description

Technical Field

[0001] This invention relates to the field of power system operation optimization technology, specifically to a method and system for efficiently solving the safety constraint unit combination based on neighborhood search. Background Technology

[0002] In the operation and management of power systems, Security Constrained Unit Commitment (SCUC) is a crucial task. Its main objective is to determine the start-up and shutdown status and power output of generating units while meeting system load demands, various security constraints, and unit operating limitations, thereby achieving economical operation of the power system. However, with the continuous expansion and increasing complexity of power systems, solving the SCUC problem faces numerous challenges.

[0003] Mathematically, SCUC belongs to the Mixed Integer Programming (MIP) problem, which is an NP-hard problem. Traditional solution methods, such as algorithms based on branch and bound frameworks, are computationally time-consuming and inefficient when dealing with large-scale systems. This is mainly because the SCUC model contains a large number of 0-1 integer variables representing the start-up and shutdown states of the units, which dramatically increases the search space in the branch and bound process; at the same time, complex cybersecurity constraints increase the size and complexity of the constraint matrix, further increasing the difficulty of solving the problem.

[0004] Currently, existing solution methods are insufficient to meet the demands of real-time dispatch and rapid decision-making in practical power systems. For example, during the clearing process of the electricity spot market, a high-precision unit combination scheme is required within a short time, but existing methods struggle to achieve this within the stipulated timeframe. Although some research has focused on improving SCUC solution algorithms, such as employing heuristic algorithms and decomposition coordination algorithms, these methods still have shortcomings in terms of solution efficiency, computational accuracy, and versatility. Therefore, developing an efficient SCUC solution method and system is of significant practical importance. Summary of the Invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problem solved by this invention is that existing safety-constrained unit combination methods based on mixed integer programming have problems such as low solution efficiency, large search space, complex parameter adjustment, and poor model feasibility, as well as how to achieve efficient iterative convergence and output of feasible scheduling schemes under the premise of ensuring network security constraints.

[0007] To address the aforementioned technical problems, this invention provides the following technical solution: an efficient solution method for secure unit combinations based on neighborhood search, comprising setting an initial number of iterations, constructing a UC model without network security constraints, and obtaining initial UC solution results; performing safety checks on transmission lines based on the UC model solution results, and selecting a specific set of generating units; constructing a neighborhood search cutting plane based on the specific set of generating units and adding it to the UC model, and resolving the UC model; performing iterative loops after resolving the UC model, and outputting the unit operating status and planned output.

[0008] As a preferred embodiment of the efficient solution method for security constraint unit combination based on neighborhood search described in this invention, the construction of the UC model without network security constraints includes: constructing the UC model without network security constraints based on the power unit parameters and load forecast data, and obtaining the initial unit output and start-up / shutdown status by solving the UC model.

[0009] As a preferred embodiment of the efficient solution method for safety constraint unit combination based on neighborhood search described in this invention, the safety verification of the transmission line includes: based on the solution results of the UC model, using the network topology and line parameters, determining whether the power flow of the line exceeds the limit through power flow calculation; if the calculated value of the power flow on the line does not exceed the thermal stability limit of the line, the safety verification is passed, the result information is output, and the process ends.

[0010] If the calculated power flow value of a line exceeds the thermal stability limit, it is determined that the power flow of the line has exceeded the limit.

[0011] As a preferred embodiment of the efficient solution method for the combination of safety constraints based on neighborhood search described in this invention, the step of selecting a specific set of generating units includes: based on the solution results of the previous round of the UC problem and grid congestion information, calculating the ability of the generating units to alleviate transmission line congestion, constructing a unit contribution index, and sorting all generating units according to their contribution to select a specific set of generating units.

[0012] As a preferred embodiment of the efficient solution method for safety constraint unit combination based on neighborhood search described in this invention, the step of constructing a neighborhood search cutting plane based on a specific unit set and adding it to the UC model includes: constructing a neighborhood search cutting plane based on the operating status of the specific unit set and adding it to the UC model to solve a new UC problem; if it is determined that the line power flow exceeds the limit, the parameters are adjusted and the solution is repeated until the calculated value of the power flow on the line does not exceed the line thermal stability limit.

[0013] As a preferred embodiment of the efficient solution method for safety constraint unit combination based on neighborhood search described in this invention, the iterative loop after resolving the UC model includes updating the iteration count and re-performing the safety check after completing the solution of the new UC problem. If the safety check fails, the subsequent steps are continued; if the safety check passes, the iteration is stopped.

[0014] As a preferred embodiment of the efficient solution method for security constraint unit combination based on neighborhood search described in this invention, the output unit operating status and planned output include, during the iterative solution process, when the reconstructed UC model passes the network security verification and meets the termination condition, the output unit output and start / stop status are obtained to obtain a scheduling scheme that meets network security constraints.

[0015] Another objective of this invention is to provide an efficient solution system for security constraint unit combination based on neighborhood search. This system can solve the problems of low solution efficiency, poor feasibility, and difficulty in balancing network security and economy in current security constraint scheduling technologies by efficiently identifying key units, constructing neighborhood search constraints, and iteratively optimizing the solution.

[0016] As a preferred embodiment of the efficient solution system for safety-constrained unit combination based on neighborhood search described in this invention, the system includes: an initial modeling module, a safety verification and unit selection module, a neighborhood search construction module, and an iterative solution and output module. The initial modeling module sets initial iteration parameters, constructs a UC model without network security constraints, and solves for the initial unit start-up / shutdown states and output schemes, providing a basic solution for subsequent iterations. The safety verification and unit selection module performs power flow safety verification on the power grid transmission lines based on the initial UC solution results and identifies specific unit sets. The neighborhood search construction module constructs a neighborhood search cutting plane and embeds it into the UC optimization model, controlling the deviation range between the current solution and historical solutions. The iterative solution and output module iteratively solves the UC problem based on the updated model, dynamically adjusts key parameters, continuously performs safety verification, and outputs a unit scheduling scheme that satisfies network security constraints.

[0017] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement a method for efficiently solving security constraint group combinations based on neighborhood search.

[0018] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of an efficient solution method for security constraint unit combinations based on neighborhood search.

[0019] The beneficial effects of this invention are as follows: The efficient solution method for safety-constrained unit combination based on neighborhood search provided by this invention effectively reduces the search space of mixed integer programming problems and reduces the invalid search range by applying the neighborhood search cutting plane. This greatly improves the solution speed of safety-constrained unit combination problems and can meet the strict requirements of real-time dispatch of power systems for solution time. Under the premise of satisfying various constraints, the unit combination scheme obtained through efficient solution can optimize the system operating cost and improve the economic benefits of the power system. Attached Figure Description

[0020] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is an overall flowchart of the efficient solution method for safety constraint unit combination based on neighborhood search provided in Embodiment 1 of the present invention. Detailed Implementation

[0022] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0023] Example 1, referring to Figure 1 As an embodiment of the present invention, an efficient solution method for safety constraint unit combination based on neighborhood search is provided, including:

[0024] S1: Set the initial number of iterations, construct a UC model without network security constraints, and obtain the initial UC solution results.

[0025] Furthermore, constructing a UC model without cybersecurity constraints involves building a UC model without cybersecurity constraints based on power unit parameters and load forecast data, and obtaining the initial unit output and start-up / shutdown status by solving the UC model.

[0026] It should be noted that in the initialization step, the number of iterations is set to 1. Based on the power unit parameters (including maximum and minimum output, ramp rate, start-up cost, minimum continuous start-up and shutdown time) and load forecast data, a UC model without network security constraints is constructed. A commercial solver is used to solve the UC model to obtain the initial unit output and start-up and shutdown status, which provides a basis for subsequent safety verification and iterative optimization.

[0027] Construct a UC model without network security constraints, including an objective function and constraints. The objective function is expressed as:

[0028]

[0029] Where N is the total number of generating units, T is the total number of time periods considered, and p i,t For the output of unit i during time period t, α i,t f represents the operating status of unit i during time period t. i,t (p i,t () represents the operating cost of unit i during time period t. This refers to the startup cost of unit i.

[0030] The constraints, including load balance constraints, are expressed as follows:

[0031]

[0032] Among them, D t This represents the load forecast value for time period t.

[0033] Positive and negative reserve capacity constraints are expressed as follows:

[0034]

[0035] in, These are the maximum and minimum allowable outputs of unit i, respectively. These represent the positive and negative reserve capacity requirements for time period i, respectively.

[0036] The spin-off reserve constraint is expressed as:

[0037]

[0038] in, These are the maximum uphill and downhill ramp rates of unit i, respectively. The requirements for adjusting the rotational backup for different time periods t are as follows:

[0039] The upper and lower limits of the unit output are expressed as follows:

[0040]

[0041] The unit ramp-up constraint is expressed as:

[0042]

[0043] The minimum continuous start-up and shutdown time constraint of the unit is expressed as:

[0044]

[0045] Among them, T i,U T i,D These are the minimum continuous start-up time and downtime of unit i, respectively; These represent the time the unit has been continuously running and shut down during time period t, respectively, and can be expressed as the unit's operating status:

[0046]

[0047] The minimum continuous start-up and shutdown time constraint of the unit is expressed as:

[0048]

[0049] Among them, T i,U T i,D These are the minimum continuous start-up time and downtime of unit i, respectively; These represent the time the unit has been continuously running and shut down during time period t, respectively, and can be expressed as the unit's operating status:

[0050]

[0051] Network security constraints are represented as follows:

[0052]

[0053] In the formula, For the thermal stability limit of the line; G l-i G is the generator output power transfer distribution factor from node i to line l; l-k D is the generator output power transfer distribution factor of the node where the bus load k is located to line l; k,t The predicted value of bus load k is given for time period t.

[0054] It should also be noted that by constructing a UC model without network security constraints and prioritizing its solution, the complexity of the model can be significantly reduced, the solution space can be narrowed, and feasible initial unit start-up and shutdown states and output schemes can be quickly obtained without considering line constraints, providing convergence direction and effective initial values ​​for subsequent iterations.

[0055] S2: Based on the solution results of the UC model, perform safety checks on the transmission lines and select a specific set of generating units.

[0056] Furthermore, the safety verification of transmission lines includes determining whether the power flow exceeds the limit by using the network topology and line parameters based on the solution results of the UC model. If the calculated power flow value on the line does not exceed the thermal stability limit of the line, the safety verification is passed, the result information is output, and the process ends.

[0057] If the calculated power flow value of a line exceeds the thermal stability limit, it is determined that the power flow of the line has exceeded the limit.

[0058] It should be noted that, based on the clearing results of the UC model without network security constraints, the power flow is calculated using network topology and line parameters to determine whether the power flow exceeds the limits. If the calculated power flow values ​​for each line do not exceed the line's thermal stability limit, the security check passes, the result information is output, and the process ends. If any line's calculated power flow value exceeds the thermal stability limit, it is determined that the line's power flow exceeds the limit, and the power flow value for each line is calculated using the node power balance equation and the line power transmission equation. For example, the thermal stability limit of a certain line is... The calculated power flow value for the line is 1100MW, which exceeds the thermal stability limit, thus indicating that the power flow is exceeding the limit. If the power flow values ​​for all lines do not exceed the thermal stability limit, the safety check passes, and the unit operating status and planned output are directly output; if there is a power flow exceeding the limit for a line, the process proceeds to the specific unit determination step.

[0059] It should also be noted that by introducing a power flow safety verification mechanism for transmission lines after each round of UC solution, the power flow distribution is calculated based on the network topology and line parameters, and it is determined whether the thermal stability limit is exceeded. This establishes a dynamic consistency constraint relationship between the UC results and the actual carrying capacity of the power grid. Under the premise of ensuring network operation safety, the impact of redundant constraints on the initial solution accuracy and speed is significantly reduced. In addition, the path branching logic driven by the verification results can achieve accurate approximation of the feasible region boundary, thereby improving the convergence efficiency and feasibility judgment accuracy of the scheduling scheme and reducing the risk of global infeasibility caused by line constraints.

[0060] Furthermore, the selection of a specific set of generating units involves, based on the solution results of the previous round of the UC problem and grid congestion information, assessing the ability of the generating units to alleviate transmission line congestion, constructing a unit contribution index, and ranking all generating units according to their contribution to select a specific set of generating units.

[0061] It should be noted that in the specific unit determination step, based on the solution results of the previous UC problem and network congestion information, the specific unit is determined by calculating the ability of each unit to alleviate transmission line congestion. When there is forward congestion, the maximum ability of unit i to alleviate line l congestion is expressed as:

[0062]

[0063] Where, ΔPl,t The ability of unit i to alleviate congestion on transmission line l during time period t. G is the solution to the current iterative UC problem. l-i This is the generator output power transfer distribution factor from the node where the generating unit is located to the line. This represents the maximum output of unit i.

[0064] When multiple transmission lines are blocked, the contribution of computer group i to alleviating the transmission line congestion is expressed as follows:

[0065]

[0066] Among them, IM i The contribution of unit i to alleviating transmission line congestion. This is a collection of transmission lines that are positively blocked during a given time period. For the time period of negative blocking transmission lines, u i G is a 0-1 variable. l-i <0, u i =1; at that time G l-i >0, u i =0, and all units are sorted in descending order according to the contribution index. The top O units that play a major role in alleviating transmission line congestion are selected to form a specific unit set.

[0067] All generating units are ranked in descending order based on their contribution index. For example, the top 20 generating units that play a major role in alleviating transmission line congestion are selected to form a specific set of generating units.

[0068] It should also be noted that by calculating the ability of each generating unit to alleviate transmission line congestion, a dynamic modeling method for matching the regulation direction with the power flow direction is introduced. This solves the problem that traditional methods fail to reflect the directionality of power flow. Under multiple time periods and line congestion scenarios, a unit contribution index is established by constructing a weighted sum of the unit's contribution capabilities on all positively congested lines and all negatively congested lines. This achieves coupled modeling of regulation capacity, regulation status, and power flow response capability. Based on the congestion relief contribution, the generating units are sorted in descending order, and the top O units that play a major role in alleviating congestion are selected to form a specific set. This significantly reduces the complexity of solving the system-wide unit participation optimization model, improves the convergence efficiency and accuracy of scheduling strategies for actual congestion path problems, and overcomes the subjectivity, locality, and low adaptability problems of traditional heuristic rule selection methods.

[0069] S3: Construct a neighborhood search cutting plane based on a specific set of units and add it to the UC model, then resolve the UC model.

[0070] Furthermore, constructing a neighborhood search cut plane based on a specific set of generating units and adding it to the UC model includes constructing a neighborhood search cut plane based on the operating status of a specific set of generating units and adding it to the UC model to solve a new UC problem. If it is determined that the power flow exceeds the limit of the line, the parameters are adjusted and the solution is repeated until the calculated value of the power flow on the line does not exceed the thermal stability limit of the line.

[0071] It should be noted that the neighborhood search cut plane constructed based on the optimal solution of the previous iteration of the UC problem is represented as follows:

[0072]

[0073] Where, α i,t It is the operating status of unit i in time period t. This is the solution to the previous iteration of the UC problem, where O is the number of critical units, T is the total number of time slots, and β is the neighborhood size control parameter with 0 ≤ β ≤ 1. After adding the cutting plane to the UC model, solve the new UC problem. If it is determined to be a line power flow violation, adjust the value of the number of critical units O or the neighborhood size control parameter β, and solve it again.

[0074] During the solution process, the value of β is dynamically adjusted according to the severity of line congestion after each iteration's safety check. β is increased when congestion is severe and decreased when congestion is mild. When β = 0, it is equivalent to fixing the unit state as the unit state of the optimal solution of the UC problem in the previous iteration, and the problem is transformed into solving a linear programming problem with network constraints. When β = 1, the neighborhood search cut plane has no effect.

[0075] For example, if the number of critical units O = 20 and the total number of time periods T = 96, the operating status of unit i in time period 3 in the previous iteration... When constructing the cut plane, based on the actual line congestion situation, the neighborhood size control parameter β = 0.3 is set to construct the neighborhood search cut plane:

[0076]

[0077] Add the cutting plane to the UC model and use a solver to solve the new UC problem. If the new problem is infeasible, for example, if constraints are violated during the solution process, adjust the number of critical units O (e.g., increase to 25) or the value of the neighborhood size control parameter β (e.g., adjust to 0.4), and solve again until a feasible solution is obtained, setting the iteration count k = k + 1. During the solution process, dynamically adjust the value of β according to the severity of line congestion after each iteration safety check. If the congestion is severe, increase β appropriately to expand the neighborhood search range; if the congestion is mild, decrease β appropriately to improve search accuracy and solution efficiency.

[0078] It should also be noted that by calculating the difference between the actual operating state of key units in each time period and the reference operating state in the previous solution, and using this difference to construct a neighborhood search cutting plane, the variable range of key units during re-solution is effectively constrained, improving the convergence and solution efficiency of the scheduling problem. When constructing the cutting plane, the number of key units and the total number of scheduling time periods are also considered, and the weighted sum of unit state changes based on the aforementioned neighborhood control parameters is used as a threshold for the acceptable disturbance range. If the weighted sum does not exceed this threshold, the current solution is considered acceptable within the neighborhood, and the scheduling solution continues; if it exceeds this threshold, the number of key units is adjusted or the neighborhood parameters are expanded, and the cutting plane is reset to ensure that a unit operation combination that meets the line safety verification conditions is obtained within a finite number of iterations, avoiding the problem of large-scale infeasible solutions caused by global random disturbances. By dynamically controlling the search range, the efficiency of the algorithm's feasibility judgment and the responsiveness of scheduling adjustments are significantly improved; localized guidance for adjusting the operating state of key units is achieved, ensuring that scheduling satisfies constraints while taking into account operational economy and stability.

[0079] S4: After resolving the UC model, perform an iterative loop to output the unit's operating status and planned output.

[0080] Furthermore, the iterative loop after resolving the UC model includes updating the iteration count and re-performing a safety check after completing the solution to the new UC problem. If the safety check fails, the subsequent steps are executed; if the safety check passes, the iteration stops.

[0081] It should be noted that after completing the model construction and solution of the new round of unit combination problem, the current solution results are iteratively updated, and the corresponding safety verification procedure is triggered to verify whether the current unit combination and its output scheme meet the safety requirements of the entire network. If the safety verification fails, the current neighborhood search mechanism is maintained, the previously constructed specific unit set and neighborhood control parameters are retained, and the next round of iterative solution process is continued on this basis; if the safety verification passes, it indicates that the current results have met the objectives of system stability, power flow constraints, and line congestion elimination, and the subsequent iterations are terminated, and the final unit operation scheduling scheme is output. By linking the aforementioned specific unit set selection, cutting plane construction, and neighborhood size control parameters, a dynamic response strategy based on congestion severity is realized, ensuring that the scheduling solution process has fast convergence and high safety, and is suitable for large-scale, multi-constraint transmission network joint optimization scheduling scenarios.

[0082] It should also be noted that by introducing a safety verification process after each round of UC model solving, and using the feedback result as the criterion for whether to continue the iteration, the scheduling solution process achieves proactive response and closed-loop control to the network-wide safety constraints, effectively improving the system's operational safety and steady-state compliance. By linking the severity of line congestion with neighborhood control parameters, a quantitative response to the network constraint tension is achieved, enabling the neighborhood search range to have adaptive expansion and convergence capabilities, and enhancing the scheduling algorithm's sensitivity and robustness to different congestion scenarios.

[0083] Furthermore, the output of unit operating status and planned output includes, during the iterative solution process, when the reconstructed UC model passes the network security check and meets the termination conditions, the output of unit output and start / stop status is obtained, thus obtaining a scheduling scheme that meets network security constraints.

[0084] It should be noted that after solving a new UC problem, the process returns to the safety verification step. If the safety verification still fails, subsequent steps such as determining key generating units, neighborhood search, and solving continue until all transmission line power flows meet the safety constraints. The final output is the operating status and planned output of the generating units that meet the safety constraints. For example, after multiple iterations, in the 5th iteration, all line power flows are within the safety range, and the output of the operating status and planned output of the generating units at this time is the result that meets the requirements. If all transmission line power flow constraints are within the safety threshold range in the 5th iteration, it indicates that the operating status of the generating units and the corresponding planned power output have met the comprehensive requirements for safe operation of the power grid, and this can be used as the final dispatch output scheme, terminating the iteration process.

[0085] It should also be noted that through closed-loop execution, multiple rounds of iterative optimization of network operation status and security indicators were achieved, ensuring that the final solution meets the constraints of the power generation plan while possessing network security feasibility and system scheduling stability.

[0086] Example 3, an embodiment of the present invention, provides an efficient solution system for safety constraint unit combination based on neighborhood search, including an initial modeling module, a safety verification and unit selection module, a neighborhood search construction module, and an iterative solution and output module.

[0087] Among them, the initial modeling module is used to set the initial iteration parameters, construct the UC model without network security constraints, and solve for the initial unit start-up and shutdown state and output scheme, providing a basic solution for subsequent iteration processes.

[0088] The safety verification and unit selection module is used to perform power flow safety verification on power grid transmission lines based on the initial UC solution results, and to identify the set of units that constitute a specific power grid.

[0089] The neighborhood search building module is used to construct the neighborhood search cutting plane and embed it into the UC optimization model to control the deviation range between the current solution and historical solutions.

[0090] The iterative solution and output module performs iterative solutions to the UC problem based on the updated model, dynamically adjusts key parameters and continuously performs safety checks, and outputs a unit scheduling scheme that meets network security constraints.

Claims

1. A method for efficiently solving safety constraint unit combination based on neighborhood search, characterized in that, include: Set the initial number of iterations, construct a UC model without network security constraints, and obtain the initial UC solution results; Based on the results of the UC model, a safety check is performed on the transmission lines to select a specific set of generating units; Based on a specific set of units, a neighborhood search cutting plane is constructed and added to the UC model, and the UC model is solved again; After resolving the UC model, an iterative loop is performed to output the unit's operating status and planned output.

2. The efficient solution method for safety constraint unit combination based on neighborhood search as described in claim 1, characterized in that: The construction of the UC model without cybersecurity constraints includes constructing a UC model without cybersecurity constraints based on power unit parameters and load forecast data, and obtaining the initial unit output and start-up / shutdown status by solving the UC model.

3. The efficient solution method for safety constraint unit combination based on neighborhood search as described in claim 2, characterized in that: The safety verification of the transmission line includes, based on the solution results of the UC model, using the network topology and line parameters, determining whether the power flow of the line exceeds the limit through power flow calculation. If the calculated value of the power flow on the line does not exceed the thermal stability limit of the line, the safety verification is passed, the result information is output, and the process ends. If the calculated power flow value of a line exceeds the thermal stability limit, it is determined that the power flow of the line has exceeded the limit.

4. The efficient solution method for safety constraint unit combination based on neighborhood search as described in claim 3, characterized in that: The selection of a specific set of generating units includes, based on the solution results of the previous round of the UC problem and grid congestion information, the ability of the computer group to alleviate transmission line congestion, constructing a unit contribution index, and sorting all units according to their contribution to select a specific set of generating units.

5. The efficient solution method for safety constraint unit combination based on neighborhood search as described in claim 4, characterized in that: The step of constructing a neighborhood search cut plane based on a specific set of generating units and adding it to the UC model includes constructing a neighborhood search cut plane based on the operating status of the specific set of generating units and adding it to the UC model to solve a new UC problem. If it is determined that the power flow of the line exceeds the limit, the parameters are adjusted and the solution is repeated until the calculated value of the power flow on the line does not exceed the thermal stability limit of the line.

6. The efficient solution method for safety constraint unit combination based on neighborhood search as described in claim 5, characterized in that: The iterative loop after resolving the UC model includes updating the iteration count and re-performing a safety check after solving the new UC problem. If the safety check fails, the subsequent steps are continued; if the safety check passes, the iteration stops.

7. The efficient solution method for safety constraint unit combination based on neighborhood search as described in claim 6, characterized in that: The output unit operating status and planned output include, during the iterative solution process, when the reconstructed UC model passes the network security verification and meets the termination conditions, the output unit output and start / stop status are obtained to obtain a scheduling scheme that meets network security constraints.

8. A system for efficiently solving safety constraint unit combination based on neighborhood search, employing the efficient solution method for safety constraint unit combination based on neighborhood search as described in any one of claims 1 to 7, characterized in that: It includes an initial modeling module, a safety verification and unit selection module, a neighborhood search construction module, and an iterative solution and output module; The initial modeling module is used to set initial iteration parameters, construct a UC model without network security constraints, and solve for the initial unit start-up and shutdown states and output schemes, providing a basic solution for subsequent iteration processes. The safety verification and unit selection module is used to perform power flow safety verification on power grid transmission lines based on the initial UC solution results, and to identify a specific set of generating units. The neighborhood search construction module is used to construct the neighborhood search cutting plane and embed it into the UC optimization model to control the deviation range between the current solution and the historical solution. The iterative solution and output module performs iterative solution of the UC problem based on the updated model, dynamically adjusts key parameters and continuously performs security checks, and outputs a unit scheduling scheme that meets network security constraints.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the efficient solution method for security constraint unit combination based on neighborhood search as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the efficient solution method for security constraint unit combination based on neighborhood search as described in any one of claims 1 to 7.

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