Standby power supply priority dynamic optimization method, system and device based on mixed integer linear programming and medium

Abstract

The invention discloses a standby power supply priority dynamic optimization method, system and device based on mixed integer linear programming and a medium, and belongs to the technical field of power system automation. The method comprises the steps of obtaining operation state data and fault state types of each standby power supply in a target power grid; based on the operation state data of each standby power supply, a mixed integer linear programming model including a target function is constructed, and the target function takes comprehensive cost minimization as an optimization target; parameters in the mixed integer linear programming model are updated according to the operation state data and the fault state type of each standby power supply; and solving the updated mixed integer linear programming model by adopting an MILP solver to obtain an optimal solution of the priority of the standby power supply. The optimal power supply switching decision is realized, the reliability and economy of the system are improved, the change of the power grid can be quickly responded, and the power supply continuity is ensured.

Description

Technical Field

[0001] This invention belongs to the field of power system automation technology, specifically relating to a method, system, device, and medium for dynamic optimization of backup power supply priority based on mixed integer linear programming. Background Technology

[0002] With the widespread application of distributed energy resources, the structure of power systems is becoming increasingly complex. In traditional centralized power supply models, the selection of backup power sources is relatively simple and fixed. However, in modern smart grids, due to the intermittency and uncertainty of renewable energy sources, as well as the diversification of user-side demands, managing multiple backup power sources efficiently and flexibly has become a major challenge.

[0003] Existing solutions typically rely on fixed priority rules or simple heuristic algorithms. The reliance on manually preset priorities fails to dynamically respond to grid conditions (such as load fluctuations or power output volatility), leading to low switching efficiency or backup power overload. Some simpler heuristic algorithms (such as genetic algorithms) suffer from slow convergence speeds and solution uncertainties, making it difficult to meet the millisecond-level response requirements of power systems. Furthermore, existing methods exhibit poor compatibility with complex constraints such as grid topology anti-circulation current and capacity limitations, requiring manual verification and resulting in low efficiency.

[0004] Therefore, there is an urgent need for a more precise and flexible method to dynamically adjust the priority of backup power supplies in order to improve the overall performance of the system. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, system, device and medium for dynamic optimization of backup power priority based on mixed integer linear programming, so as to achieve optimal power switching decision, which not only improves the reliability and economy of the system, but also enables rapid response to changes in the power grid and ensures power supply continuity.

[0006] This invention provides the following technical solution:

[0007] Firstly, a dynamic optimization method for backup power supply priority based on mixed integer linear programming is provided, including: obtaining the operating status data and fault status type of each backup power supply in the target power grid;

[0008] Based on the operating status data of each backup power source, a mixed integer linear programming model is constructed, which includes an objective function with the goal of minimizing the overall cost.

[0009] Update the parameters in the mixed integer linear programming model based on the operating status data and fault status type of each backup power source;

[0010] The updated mixed-integer linear programming model is solved using the MILP solver to obtain the optimal solution for backup power priority.

[0011] As an optional technical solution of the present invention, the operating status data of the backup power supply includes the economic cost of the backup power supply, the reliability score of the backup power supply, the priority weight of the backup power supply, and the estimated switching time of the backup power supply.

[0012] The fault status types include transient short-circuit faults and equipment aging.

[0013] As an optional technical solution of the present invention, the step of constructing a mixed-integer linear programming model including an objective function based on the operating status data of each backup power source includes:

[0014] The objective function is expressed as:

[0015] ;

[0016] ;

[0017] in, Indicates total cost. This represents the priority of the backup power source that minimizes the overall cost. Indicates the first The priority of each backup power source is determined by α, which represents the time cost weight. Indicates the total number of backup power supplies. Indicates the first A backup power supply This represents the priority weight of the i-th backup power source. This represents the estimated switching time for the i-th backup power source. This represents the economic cost of the i-th backup power source. Let represent the reliability score of the i-th backup power source, β represent the economic weight, and γ represent the reliability weight. Indicates whether to select the i-th backup power source. This indicates that the i-th backup power source is selected. This indicates that the i-th backup power source was not selected.

[0018] As an optional technical solution of the present invention, the mixed-integer linear programming model further includes constraints, which include:

[0019] Load capacity requirement constraints are expressed as follows:

[0020] ;

[0021] The weight normalization constraint is expressed as:

[0022] ;

[0023] ;

[0024] Anti-circulating current constraint means that if there is a circulating current risk between the i-th backup power supply and the j-th backup power supply, they are prohibited from being used simultaneously, as expressed as:

[0025] ;

[0026] Action timing constraints are represented as follows:

[0027] ;

[0028] Power availability constraints stipulate that if the i-th backup power supply fails, then the i-th backup power supply will not be selected, as expressed as:

[0029] ;

[0030] in, This represents the output of the i-th backup power source. Indicates the total load. Indicates whether to select the j-th backup power source. Let M represent the priority weight of the j-th backup power source, and M represent a maximum constant. This indicates whether the i-th backup power supply is put into operation before the j-th backup power supply.

[0031] As an optional technical solution of the present invention, updating the parameters in the mixed integer linear programming model based on the operating status data and fault status type of each backup power supply includes:

[0032] Based on the real-time acquired fault status type, update the time cost weight α, economic weight β, and reliability weight γ in the objective function. If it is a transient short-circuit fault, then update to... , , If the equipment is outdated, then replace it with... , , ;

[0033] Based on the real-time operational status data of each backup power source, the economic cost, reliability score, priority weight, and estimated switching time of the backup power source in the mixed-integer linear programming model are updated at preset intervals.

[0034] Secondly, a dynamic optimization system for backup power priority based on mixed integer linear programming is provided, including: an acquisition module for acquiring the operating status data and fault status type of each backup power source in the target power grid;

[0035] The module is used to construct a mixed-integer linear programming model based on the operating status data of each backup power source, including an objective function, wherein the objective function is optimized to minimize the overall cost.

[0036] The update module is used to update the parameters in the mixed integer linear programming model based on the operating status data and fault status type of each backup power source.

[0037] The solver module is used to solve the updated mixed-integer linear programming model using the MILP solver to obtain the optimal solution for backup power priority.

[0038] Thirdly, a dynamic optimization device for backup power priority based on mixed integer linear programming is provided, comprising a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method described in the first aspect.

[0039] Fourthly, a computer-readable storage medium is provided having a computer program stored thereon, characterized in that the program, when executed by a processor, implements the steps of the method described in the first aspect.

[0040] Compared with the prior art, the beneficial effects of the present invention are:

[0041] This invention provides a dynamic optimization method for backup power priority based on mixed-integer linear programming. By constructing a mixed-integer linear programming model, it can provide a global optimal solution, ensuring the accuracy of the scheduling scheme. It avoids the randomness and tendency to get trapped in local optima of heuristic algorithms, and overcomes the shortcomings of relying on manually preset fixed priorities and being unable to dynamically respond to grid conditions. It improves the applicability and robustness to complex constraints and dynamic environments. Attached Figure Description

[0042] Figure 1 This is a flowchart of a dynamic optimization method for backup power priority based on mixed integer linear programming in an embodiment of the present invention. Detailed Implementation

[0043] The present invention will be further described below with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and should not be used to limit the scope of protection of the present invention.

[0044] Example 1

[0045] This embodiment provides a dynamic optimization method for backup power priority based on mixed-integer linear programming. For example... Figure 1 As shown, it includes the following steps:

[0046] Step 1: Obtain the operating status data and fault status type of each backup power source in the target power grid.

[0047] The operational status data of the backup power supply includes its economic cost, reliability score, priority weight, and estimated switchover time. The fault status types include transient short-circuit faults and equipment aging.

[0048] Step 2: Based on the operating status data of each backup power source, construct a mixed integer linear programming model including an objective function, wherein the objective function aims to minimize the overall cost.

[0049] The objective function is expressed as:

[0050] ;

[0051] ;

[0052] in, Indicates total cost. This represents the priority of the backup power source that minimizes the overall cost. Indicates the first The priority of each backup power source is determined by α, which represents the time cost weight. Indicates the total number of backup power supplies. Indicates the first A backup power supply This represents the priority weight of the i-th backup power source; a larger value indicates a higher priority. This represents the estimated switching time for the i-th backup power source, including startup delay and circuit breaker tripping time. This represents the economic cost of the i-th backup power source. Let represent the reliability score of the i-th backup power source, β represent the economic weight, and γ represent the reliability weight. Indicates whether to select the i-th backup power source. This indicates that the i-th backup power source is selected. This indicates that the i-th backup power source was not selected.

[0053] In this embodiment, due to the introduction of binary variables The solution time may increase exponentially with the size, so N≤10 is set.

[0054] The mixed-integer linear programming model also includes constraints, which include:

[0055] Load capacity requirement constraints are expressed as follows:

[0056] ;

[0057] The weight normalization constraint is expressed as:

[0058] ;

[0059] ;

[0060] Anti-circulating current constraint means that if there is a circulating current risk between the i-th backup power supply and the j-th backup power supply, they are prohibited from being used simultaneously, as expressed as:

[0061] ;

[0062] Action timing constraints: Higher priority power supplies must be put into operation first, as shown below:

[0063] ;

[0064] Power availability constraints stipulate that if the i-th backup power supply fails, then the i-th backup power supply will not be selected, as expressed as:

[0065] ;

[0066] in, This represents the output of the i-th backup power source. Indicates the total load. Indicates whether to select the j-th backup power source. Let M represent the priority weight of the j-th backup power source, and M represent a maximum constant. This indicates whether the i-th backup power supply was put into operation before the j-th backup power supply. This indicates that the i-th backup power supply is put into operation before the j-th backup power supply; otherwise, it is 0.

[0067] Step 3: Update the parameters in the mixed integer linear programming model based on the operating status data and fault status type of each backup power source.

[0068] Based on the real-time acquired fault status type, update the time cost weight α, economic weight β, and reliability weight γ in the objective function. If it is a transient short-circuit fault, then update to... , , If the equipment is outdated, then replace it with... , , .

[0069] Based on the real-time operational status data of each backup power source, the economic cost, reliability score, priority weight, and estimated switching time of each backup power source in the mixed-integer linear programming model are updated at preset intervals. In this embodiment, the update occurs every 50ms.

[0070] Step 4: Use the MILP solver to solve the updated mixed-integer linear programming model to obtain the optimal solution for backup power priority.

[0071] In this embodiment, the Gurobi solver is used for solving the problem. An optimal priority list is generated based on the optimal solution, and the circuit breaker is controlled to operate according to this list via a GOOSE message.

[0072] Example 2

[0073] This embodiment provides a dynamic optimization system for backup power priority based on mixed-integer linear programming, including:

[0074] The acquisition module is used to acquire the operating status data and fault status types of each backup power source in the target power grid;

[0075] The module is used to construct a mixed-integer linear programming model based on the operating status data of each backup power source, including an objective function, wherein the objective function is optimized to minimize the overall cost.

[0076] The update module is used to update the parameters in the mixed integer linear programming model based on the operating status data and fault status type of each backup power source.

[0077] The solver module is used to solve the updated mixed-integer linear programming model using the MILP solver to obtain the optimal solution for backup power priority.

[0078] Example 3

[0079] This embodiment provides a dynamic optimization device for backup power priority based on mixed-integer linear programming, including a processor and a storage medium. The storage medium is used to store instructions. The processor is used to perform operations according to the instructions to execute the steps of the dynamic optimization method for backup power priority based on mixed-integer linear programming described in Embodiment 1.

[0080] Example 4

[0081] This embodiment provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the backup power priority dynamic optimization method based on mixed integer linear programming described in Embodiment 1.

[0082] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0083] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0084] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0085] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0086] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A dynamic optimization method for backup power supply priority based on mixed-integer linear programming, characterized in that, include: Acquire the operating status data and fault status types of each backup power source in the target power grid; Based on the operating status data of each backup power source, a mixed integer linear programming model is constructed, which includes an objective function with the goal of minimizing the overall cost. Update the parameters in the mixed integer linear programming model based on the operating status data and fault status type of each backup power source; The updated mixed-integer linear programming model is solved using the MILP solver to obtain the optimal solution for backup power priority.

2. The method for dynamic optimization of backup power supply priority based on mixed-integer linear programming according to claim 1, characterized in that, The operating status data of the backup power supply includes the economic cost of the backup power supply, the reliability score of the backup power supply, the priority weight of the backup power supply, and the estimated switching time of the backup power supply. The fault status types include transient short-circuit faults and equipment aging.

3. The method for dynamic optimization of backup power supply priority based on mixed-integer linear programming according to claim 2, characterized in that, Based on the operating status data of each backup power source, a mixed-integer linear programming model including an objective function is constructed, comprising: The objective function is expressed as: ； ； in, Indicates total cost. This represents the priority of the backup power source that minimizes the overall cost. Indicates the first The priority of each backup power source is determined by α, which represents the time cost weight. Indicates the total number of backup power supplies. Indicates the first A backup power supply This represents the priority weight of the i-th backup power source. This represents the estimated switching time for the i-th backup power source. This represents the economic cost of the i-th backup power source. Let represent the reliability score of the i-th backup power source, β represent the economic weight, and γ represent the reliability weight. Indicates whether to select the i-th backup power source. This indicates that the i-th backup power source is selected. This indicates that the i-th backup power source was not selected.

4. The method for dynamic optimization of backup power supply priority based on mixed-integer linear programming according to claim 3, characterized in that, The mixed-integer linear programming model also includes constraints, which include: Load capacity requirement constraints are expressed as follows: ； The weight normalization constraint is expressed as: ； ； Anti-circulating current constraint means that if there is a circulating current risk between the i-th backup power supply and the j-th backup power supply, they are prohibited from being used simultaneously, as expressed as: ； Action timing constraints are represented as follows: ； Power availability constraints stipulate that if the i-th backup power supply fails, then the i-th backup power supply will not be selected, as expressed as: ； in, This represents the output of the i-th backup power source. Indicates the total load. Indicates whether to select the j-th backup power source. Let M represent the priority weight of the j-th backup power source, and M represent a maximum constant. This indicates whether the i-th backup power supply is put into operation before the j-th backup power supply.

5. The method for dynamic optimization of backup power supply priority based on mixed-integer linear programming according to claim 4, characterized in that, The step of updating the parameters in the mixed-integer linear programming model based on the operating status data and fault status type of each backup power source includes: Based on the real-time acquired fault status type, update the time cost weight α, economic weight β, and reliability weight γ in the objective function. If it is a transient short-circuit fault, then update to... , , If the equipment is outdated, then replace it with... , , ; Based on the real-time operational status data of each backup power source, the economic cost, reliability score, priority weight, and estimated switching time of the backup power source in the mixed-integer linear programming model are updated at preset intervals.

6. A dynamic optimization system for backup power supply priority based on mixed-integer linear programming, characterized in that, include: The acquisition module is used to acquire the operating status data and fault status types of each backup power source in the target power grid; The module is used to construct a mixed-integer linear programming model based on the operating status data of each backup power source, including an objective function, wherein the objective function is optimized to minimize the overall cost. The update module is used to update the parameters in the mixed integer linear programming model based on the operating status data and fault status type of each backup power source. The solver module is used to solve the updated mixed-integer linear programming model using the MILP solver to obtain the optimal solution for backup power priority.

7. A dynamic optimization device for backup power supply priority based on mixed-integer linear programming, characterized in that, Including processor and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the backup power priority dynamic optimization method based on mixed integer linear programming as described in any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the dynamic optimization method for backup power priority based on mixed integer linear programming as described in any one of claims 1 to 5.

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