A method, device and equipment for optimizing spare parts procurement of a power enterprise and a storage medium
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- LANGFANG POWER SUPPLY COMPANY STATE GRID JIBEI ELECTRIC POWER COMPANY
- Filing Date
- 2026-01-16
- Publication Date
- 2026-06-05
AI Technical Summary
Existing spare parts procurement methods for power companies are ill-equipped to handle the uncertainty of sudden demand for relay protection spare parts. They lack robustness in extreme scenarios, leading to procurement strategies that are prone to failure and wasted costs. Furthermore, traditional methods fail to effectively combine the randomness of component failures with the uncertainty of demand, resulting in low decision-making accuracy and low solution efficiency.
A two-layer robust optimization model is constructed with the goal of minimizing total procurement cost. Extremely reasonable failure scenarios are screened through a boundary scenario generation algorithm. Combined with probability and quantity constraints, the model is transformed into a single-layer optimization model and solved efficiently using a mathematical programming solver to generate a procurement list and execution plan.
It improves the accuracy and robustness of spare parts procurement, ensures rapid grid response under extreme faults, avoids blind procurement and spare parts shortages, and supports lean operation of power companies.
Smart Images
Figure CN122155591A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of power grid technology, and in particular to a method, apparatus, equipment, and storage medium for optimizing the procurement of spare parts by power companies. Background Technology
[0002] In the relay protection and automated operation and maintenance work of power grid enterprises, with the widespread adoption of smart substations and the continuous emergence of new protection devices, the types of power equipment spare parts are becoming increasingly diverse, with varied manufacturers, sources, and lifespan characteristics, posing a severe challenge to spare parts inventory management. The safe and stable operation of the power system highly depends on the reliability and rapid repairability of critical equipment, and a scientific and reasonable spare parts procurement strategy is a key link in ensuring maintenance efficiency and controlling enterprise operating costs. The traditional spare parts management model, which relies on manual experience, is no longer suitable for the lean operation requirements of modern power grids, and there is an urgent need to introduce more accurate and efficient optimization methods to support it.
[0003] Currently, numerous studies have proposed solutions from different perspectives for optimizing spare parts management in power companies. For example, some studies have used support vector machine regression prediction and ABC classification to establish inventory quota models, but these assume continuous demand, making it difficult to cope with the sudden demand for relay protection spare parts. Other studies have constructed Markov models based on the component update process or established inventory models that consider procurement, storage, and stockout costs, but the former does not fully consider the actual situation of difficult spare parts recovery, while the latter fails to effectively characterize the uncertainty of demand. In addition, some studies have attempted to introduce optimization algorithms such as Monte Carlo simulation, marginal benefit analysis, and distribution estimation algorithms for spare parts allocation, but most of these methods do not systematically address the randomness of spare parts failure from the perspective of uncertainty modeling, nor do they provide sufficient robustness guarantees under extreme failure scenarios.
[0004] Overall, existing methods for optimizing spare parts procurement suffer from low accuracy. Summary of the Invention
[0005] This application provides a method, apparatus, equipment, and storage medium for optimizing spare parts procurement in power companies, which can improve the accuracy of spare parts procurement.
[0006] To achieve the above objectives, this application adopts the following technical solution: Firstly, this application provides a method for optimizing spare parts procurement for power companies, including: Obtain operational data and spare parts unit procurement costs for various components in the power grid; Based on the aforementioned operational data and spare parts unit procurement costs, a two-layer robust optimization model for spare parts procurement is established under the constraints of component failure scenarios, with the objective of minimizing total procurement costs. A set of boundary fault scenarios that satisfy the aforementioned constraints is generated using a boundary scenario generation algorithm. Based on the set of boundary fault scenarios, operational constraints are constructed and added to the initial optimization model to form a single-layer optimization model equivalent to the two-layer robust optimization model. The optimal spare parts procurement strategy is obtained by solving the single-layer optimization model.
[0007] Optionally, generating a set of boundary fault scenarios that satisfy the constraints using a boundary scenario generation algorithm includes: Start testing with a single component failure scenario; Gradually increase the number of components that fail simultaneously to generate new test scenarios; When the test scenario does not meet the constraints, the expansion stops and the last scenario that meets the constraints is added to the boundary fault scenario set. Iterate through all possible combinations of components to generate the final set of boundary fault scenarios.
[0008] Optionally, the construction of operational constraints based on the set of boundary fault scenarios includes: For each scenario in the set of boundary fault scenarios, for any spare part type g, construct an operational constraint that the procurement quantity of spare part type g is greater than or equal to the sum of the number of faults of all components belonging to spare part type g in the corresponding scenario.
[0009] Optionally, the solution of the single-layer optimization model is accomplished using a mathematical programming solver, which includes a linear programming solver or an integer programming solver.
[0010] Optionally, the constraints of the component failure scenario include probability constraints and quantity constraints; The probability constraint is that the joint probability of multiple faults occurring simultaneously is less than or equal to the probability threshold. The quantity constraint is defined as the total number of components that fail within the same time period being less than or equal to a quantity threshold.
[0011] Optionally, the operating data includes component types, operating quantities, and failure probability distribution.
[0012] Optionally, the method further includes: Based on the optimal spare parts procurement strategy, a procurement list and execution plan are generated; Based on the aforementioned procurement list and execution plan, complete the actual procurement and inventory configuration of spare parts.
[0013] Secondly, this application provides a device for optimizing spare parts procurement for power companies, comprising: The acquisition module is used to acquire operating data of various components in the power grid and the unit procurement cost of spare parts; The processing module is used to establish a two-layer robust optimization model for spare parts procurement with the objective of minimizing the total procurement cost, based on the operational data and the unit procurement cost of spare parts, under the constraints of component failure scenarios; generate a set of boundary failure scenarios that satisfy the constraints through a boundary scenario generation algorithm; construct operational constraints based on the set of boundary failure scenarios, and add the operational constraints to the initial optimization model to form a single-layer optimization model equivalent to the two-layer robust optimization model; The determination module is used to obtain the optimal spare parts procurement strategy by solving the single-layer optimization model.
[0014] Thirdly, this application provides a computing device, including a memory and a processor; The memory stores one or more computer programs, the one or more computer programs including instructions; when the instructions are executed by the processor, the computing device performs the method as described in any one of the first aspects.
[0015] Fourthly, this application provides a computer-readable storage medium for storing a computer program for performing the method as described in any one of the first aspects.
[0016] As can be seen from the above technical solution, this application has at least the following beneficial effects: First, by acquiring component operation data and spare parts unit procurement costs, and combining probability constraints and quantity constraints, a two-layer robust optimization model with a clear objective is constructed. This model aims to minimize the total procurement cost while fully considering the randomness of power grid component failures, thus breaking the idealized assumptions of demand distribution in traditional methods.
[0017] Secondly, a boundary scenario generation algorithm based on component-by-component testing is adopted to traverse all reasonable fault combinations and filter out the set of boundary fault scenarios, ensuring that the model can cover extreme but feasible fault situations. This significantly improves the robustness of the procurement strategy to uncertain faults and solves the problem that existing methods are unable to cope with sudden faults.
[0018] Furthermore, by constructing operational constraints that ensure the procurement quantity is not less than the scenario failure requirements and transforming them into a single-layer optimization model, and using a mature mathematical programming solver to achieve efficient solution, the accuracy of decision-making is guaranteed, while avoiding the problem of low solution efficiency of complex models.
[0019] Finally, by generating procurement lists and execution plans, a closed-loop implementation from strategy optimization to actual procurement and inventory allocation was achieved. This not only ensured the rapid response and reliable execution of power system maintenance work, contributing to the safe and stable operation of the power grid, but also avoided cost waste caused by blind procurement or spare parts shortages by accurately controlling the procurement quantity, providing strong support for the lean operation of power companies.
[0020] It should be understood that the descriptions of technical features, technical solutions, beneficial effects, or similar language in this application do not imply that all features and advantages can be achieved in any single embodiment. Rather, it is understood that the description of a feature or beneficial effect means that a specific technical feature, technical solution, or beneficial effect is included in at least one embodiment. Therefore, the descriptions of technical features, technical solutions, or beneficial effects in this specification do not necessarily refer to the same embodiment. Furthermore, the technical features, technical solutions, and beneficial effects described in this embodiment can be combined in any suitable manner. Those skilled in the art will understand that embodiments can be implemented without one or more specific technical features, technical solutions, or beneficial effects of a particular embodiment. In other embodiments, additional technical features and beneficial effects may be identified in specific embodiments that do not embody all embodiments. Attached Figure Description
[0021] Figure 1 A flowchart illustrating a method for optimizing spare parts procurement for power companies, provided as an embodiment of this application; Figure 2 A schematic diagram of a device for optimizing spare parts procurement for power companies, provided as an embodiment of this application; Figure 3 This is a schematic diagram of a computing device provided in an embodiment of this application. Detailed Implementation
[0022] The terms "first," "second," and "third," etc., used in this application specification and accompanying drawings are used to distinguish different objects, not to limit a specific order.
[0023] In the embodiments of this application, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.
[0024] To ensure clarity and conciseness in the description of the following embodiments, a brief introduction to the related technologies is given first: Power company spare parts refer to the spare components (such as motherboards, sensors, communication modules, etc.) of equipment such as relay protection devices and automation equipment in the power system. They are used for rapid replacement after equipment failure and are key materials to ensure the continuous and stable operation of the power grid.
[0025] In the relay protection and automation operation and maintenance of power grid enterprises, existing spare parts procurement optimization methods face major technical problems: they are difficult to adapt to the sudden and irregular demand characteristics of relay protection spare parts, have weak ability to cope with the uncertainty of demand, and are prone to falling into the dual contradiction of blind procurement leading to cost waste and spare parts shortages delaying maintenance; at the same time, existing methods do not systematically consider the randomness of component failures, have insufficient robustness in extreme scenarios such as multi-component joint failures, and procurement strategies are prone to failure. In addition, some methods rely on ideal assumptions such as continuous distribution of demand, resulting in low decision accuracy, while other methods fall into the dilemma of low solution efficiency due to complex scenario design. It is difficult to balance optimization accuracy and implementation feasibility, and cannot meet the requirements of modern power grid lean operation for the accuracy, risk controllability, and efficiency of spare parts procurement.
[0026] The main causes of the above problems are as follows: the fault demand of relay protection spare parts is affected by multiple factors such as equipment aging and sudden failures, and there is no fixed distribution pattern. However, existing methods mostly model based on ideal assumptions such as the continuity of demand, or directly ignore the uncertainty of demand, resulting in a serious disconnect between the model and the actual scenario. Moreover, existing methods lack a dual constraint system of probability and quantity. Either the fault scenarios are not properly screened, resulting in excessive model complexity, or key extreme scenarios are omitted, resulting in insufficient robustness. At the same time, some methods have not designed an appropriate optimization structure for uncertainty issues, nor have they found an efficient model transformation path. In addition, the problem of the dependence on manual experience in the traditional model and the disconnect between the component operation data accumulated by the power grid ultimately makes it difficult for existing procurement optimization methods to adapt to the actual operation needs of modern power grids.
[0027] In view of this, embodiments of this application provide a method for optimizing spare parts procurement for power companies, which can be executed by a processing device. This processing device can be a terminal or a server. Terminals include, but are not limited to, smartphones, tablets, laptops, personal digital assistants, or smart wearable devices. Servers can be cloud servers, such as central servers in a central cloud computing cluster or edge servers in an edge cloud computing cluster. Alternatively, servers can be located in a local data center. A local data center refers to a data center directly controlled by the user.
[0028] To address the challenges of existing power company spare parts procurement optimization methods, such as insufficient robustness to extreme scenarios and an imbalance between accuracy and solution efficiency, this application firstly leverages the accumulated component operation data and spare parts cost data from the power grid to break away from the idealized assumptions of traditional methods and construct a realistic optimization foundation. Secondly, it introduces robust optimization principles to design a two-layer model: the upper layer aims to minimize total procurement costs, while the lower layer focuses on risk management in extreme failure scenarios, achieving a balance between cost optimization and risk control. Next, a boundary scenario generation algorithm is used to select extreme and reasonable scenarios that satisfy both constraints, ensuring the model's coverage of sudden failures while avoiding solution complexity caused by scenario redundancy. Finally, the two-layer model is transformed into a single-layer model that can be efficiently solved using mature solvers, extending to the implementation of procurement lists and execution plans, forming a complete closed loop from strategy optimization to practical application, ultimately providing an accurate, robust, and implementable spare parts procurement optimization solution.
[0029] To make the technical solution of this application clearer and easier to understand, the following describes a method for optimizing spare parts procurement for power companies, in conjunction with the accompanying drawings. Figure 1 As shown, this figure is a flowchart of a method for optimizing spare parts procurement in power companies, provided by an embodiment of this application. The method includes: S201. The processing equipment acquires the operating data of various components in the power grid and the unit procurement cost of spare parts.
[0030] Various components in the power grid refer to the constituent parts of equipment such as relay protection devices and automation equipment in the power grid system. For example, the motherboard of the relay protection device, the sensors and communication modules of the automation terminal, etc., are the fault carriers corresponding to spare parts, and their operating status directly determines the demand for spare parts.
[0031] Operational data refers to quantitative data reflecting the operating status and fault characteristics of components. Operational data includes component type, operational quantity, and fault probability distribution. Component type refers to the specific component type of various equipment in the power grid, such as the mainboard of a relay protection device, sensors, and communication modules, used to distinguish the fault carriers corresponding to different spare parts. Operational quantity is the actual total number of a certain type of component deployed in the power grid, such as the number of a certain type of sensor installed in the entire network, which is the basis for statistically analyzing the scale of fault demand. Fault probability distribution is the probability law of component failure within a specific time period, such as a distribution model fitted based on historical data, used to quantify the uncertainty of component failure.
[0032] The unit procurement cost of spare parts refers to the unit price (unit: yuan / piece) when purchasing a single spare part of a certain specification. The data comes from the power company's material procurement ledger, supplier annual quotation sheets, etc., and is the basic parameter for calculating the total procurement cost and constructing optimization targets.
[0033] The specific data acquisition steps are as follows: The processing equipment connects to existing data sources such as PMS2.0, equipment fault record databases, and material procurement management systems. It acquires operational data, including the types, quantities, and fault probability distributions of various components, through automatic extraction or manual input. Simultaneously, it collects the unit purchase price for each spare part. After data collection, the processing equipment cleans the data (removing outliers) and standardizes it (unifying data format and statistical dimensions).
[0034] These operations ensure the accuracy and consistency of the data, ultimately forming a structured dataset that meets the needs of model building.
[0035] S202. Based on the operating data and the unit procurement cost of spare parts, and under the constraint of component failure scenarios, a two-layer robust optimization model for spare parts procurement is established with the goal of minimizing the total procurement cost.
[0036] The constraints of component failure scenarios are dual constraints that limit the reasonable range of failures. The constraints of component failure scenarios include probability constraints and quantity constraints. The probability constraint is that the joint probability of multiple failures occurring at the same time is less than or equal to the probability threshold. The quantity constraint is that the total number of components that fail within the same time period is less than or equal to the quantity threshold.
[0037] Total procurement cost is the total cost of purchasing all spare parts, which is equal to the sum of the product of the unit procurement cost of each type of spare part and the corresponding procurement quantity.
[0038] The two-layer robust optimization model for spare parts procurement is a mathematical model containing two layers of optimization logic. The upper layer determines the quantity of spare parts to be procured with the goal of minimizing the total procurement cost, while the lower layer focuses on verifying the feasibility of the strategy in extreme failure scenarios, achieving a balance between optimal cost and controllable risk.
[0039] The processing equipment uses operational data and spare parts unit procurement costs as inputs to construct a robust optimization model with a two-layer structure within a reasonable fault range defined by probability and quantity constraints. The goal of this model is to minimize the total spare parts procurement cost for power companies. During model construction, the processing equipment first quantifies the uncertainty of component failures using operational data, then clarifies the basis for cost calculations by combining spare parts unit procurement costs. Simultaneously, it uses dual constraints to filter out extreme fault scenarios that exceed the actual risk range, ensuring that the model both closely reflects the actual operation of the power grid and can withstand fault risks within a reasonable range.
[0040] The objective function for minimizing total procurement cost is:
[0041] in, This represents the total procurement cost. This represents a set of spare parts types. Indicates spare parts type The unit procurement cost Indicates spare parts type The quantity of goods purchased.
[0042] The expression for the probability constraint in the constraint conditions is:
[0043] in, Represents a set of components. Indicator element At any moment The probability of failure, This represents the probability threshold.
[0044] The expression for the quantity constraint in the constraint conditions is:
[0045] in, Represents a set of components. Indicator element At any moment The fault status, This indicates that the component is functioning normally. Indicates component failure. This indicates the quantity threshold.
[0046] The computational expression for the two-layer robust optimization model is:
[0047] in, Indicates spare parts type The purchase quantity is a non-negative integer. Indicator element The corresponding spare part type for a fault is g, i.e., component. The fault requires replacement of the spare part (g).
[0048] S203. The processing device generates a set of boundary fault scenarios that meet the constraints through a boundary scenario generation algorithm.
[0049] Boundary scenario generation algorithm is a computational method for filtering extremely reasonable fault scenarios. It finds the worst fault combination that satisfies the constraints by gradually expanding the number of faulty components.
[0050] Boundary failure scenarios are extreme combinations of failures that meet the constraints. They represent scenarios with the greatest failure demand but within a reasonable risk range, and can fully verify the risk resistance capability of the procurement strategy.
[0051] The boundary fault scenario set is a collection of all boundary fault scenarios that meet the requirements, and it serves as the input for subsequent construction of operational constraints and optimization models.
[0052] Specifically, the test begins with a single component failure scenario; the number of components that fail simultaneously is gradually increased to generate new test scenarios; when a test scenario no longer meets the constraints, the expansion stops, and the last scenario that meets the constraints is added to the boundary failure scenario set; all possible component combinations are traversed to generate the final boundary failure scenario set.
[0053] The processing equipment operates a boundary scenario generation algorithm. Following a logic from simple to complex, it progressively generates and verifies fault scenarios, ultimately selecting all extremely reasonable fault scenarios that satisfy both probability and quantity constraints, forming a boundary fault scenario set. Specifically, the algorithm first tests simple scenarios with single-component failures, then gradually increases the number of simultaneously failing components to generate new scenarios. Each generated scenario is verified to meet the dual constraints. If a scenario does not meet the constraints, scenario expansion in that direction stops, and the last scenario that met the constraints before expansion is added to the set. After traversing all possible combinations of component failures, a complete boundary fault scenario set is finally formed.
[0054] The algorithm begins with the simplest failure scenario, considering only a single component failure, as the initial test scenario to lay the foundation for subsequent expansion. Based on this initial scenario, the algorithm progressively increases the number of components failing simultaneously according to preset rules, for example, expanding from one component failure to two, three, and so on, generating more complex multi-component joint failure test scenarios. Each time a new test scenario is generated, the algorithm checks whether it meets probability and quantity constraints. If a scenario does not meet the constraints, it indicates that the scenario exceeds a reasonable risk range, and the expansion of the number of faulty components in that direction is immediately stopped. Simultaneously, the last scenario that satisfied the constraints before expansion is identified as a boundary failure scenario and added to the set.
[0055] Repeat the above process of expansion, verification, and filtering, traversing all possible component fault combinations in the power grid to ensure that no component combination that may form a boundary fault scenario is missed. Finally, integrate all the filtered boundary fault scenarios to form a complete set of boundary fault scenarios.
[0056] This process ensures that the ensemble energy covers the worst-case fault scenarios that may be encountered in the actual operation of the power grid, while also eliminating unreasonable scenarios that exceed actual risks through constraints, thus providing accurate and comprehensive scenario inputs for subsequent model optimization.
[0057] S204. The processing equipment constructs operational constraints based on the set of boundary fault scenarios and adds the operational constraints to the initial optimization model to form a single-layer optimization model equivalent to the two-layer robust optimization model.
[0058] Operational constraints are quantitative limitations set to ensure that the procurement strategy can cover extreme failure requirements. They specify that the quantity of spare parts procured must meet the maximum demand under boundary failure scenarios and are key constraints that connect scenarios and models.
[0059] The initial optimization model refers to the prototype of a two-layer robust optimization model that was previously constructed, which only includes the objective function (minimizing the total procurement cost) and basic constraints (probabilistic constraints and quantity constraints), and has not yet incorporated the requirements of specific scenarios.
[0060] Equivalent transformation refers to transforming a complex double-nested model into a simple single-layer model without changing the model's optimization objective and constraint logic. The optimal solution of the transformed model is completely consistent with that of the original double-layer model.
[0061] A single-layer optimization model is a mathematical model containing only one layer of optimization logic. All constraints are directly related to the decision variable (the quantity of spare parts to be purchased). There is no need for nested solutions, and it can be efficiently calculated using a conventional mathematical programming solver.
[0062] Based on the set of boundary fault scenarios, the processing equipment first formulates corresponding requirement guarantee constraints for each boundary scenario, then adds these constraints to the initial two-layer robust optimization model, eliminates the two-layer nested structure of the original model through mathematical transformation, and finally forms a single-layer optimization model that is completely consistent with the optimal solution of the original model.
[0063] For each scenario in the set of boundary fault scenarios, for any spare part type g, construct an operational constraint that the procurement quantity of spare part type g is greater than or equal to the sum of the number of faulty components belonging to spare part type g in the corresponding scenario. For each extremely reasonable fault scenario in the set of boundary fault scenarios, regardless of the spare part type (denoted as spare part type g), ensure that the procurement quantity of this type of spare part is sufficient to cover the total number of faulty components that need to be replaced with this type of spare part in this scenario.
[0064] The specific implementation logic is as follows: First, for each boundary fault scenario in the set, the processing equipment counts the number of fault requirements for various spare parts in that scenario (i.e., the total number of faults of the corresponding components), and constructs an operational constraint for each type of spare part where the procurement quantity is not less than the required quantity in that scenario; Second, the operational constraints corresponding to all boundary scenarios are added to the initial optimization model one by one, replacing the nested logic of maximizing fault requirements in the lower layer of the original two-layer model; Third, through equivalent transformation rules, such as replacing uncertain variables with scenario enumeration, the original two-layer nested structure is decomposed into a single-layer model containing only decision variables and explicit constraints.
[0065] This process retains the original model's ability to withstand extreme failure risks while solving the problems of complex and inefficient two-layer model solutions, laying the foundation for rapid solutions to the optimal procurement strategy in the future.
[0066] S205. The processing equipment obtains the optimal spare parts procurement strategy by solving the single-layer optimization model.
[0067] Solving the single-layer optimization model is accomplished using a mathematical programming solver, which includes a linear programming solver or an integer programming solver.
[0068] Mathematical programming solvers are professional software tools that can solve mathematical models. They can automatically analyze the objective function and constraints of the model and quickly calculate the optimal solution using mature algorithms, without the need for manual calculation.
[0069] Linear programming solvers are tools for solving models where both the objective function and constraints are linear. They are designed to solve optimization problems with linear objectives and constraints, offering high computational efficiency and accurate results.
[0070] The integer programming solver is based on the linear programming solution logic, but adds an adaptation function that requires decision variables to be integers, making it suitable for real-world scenarios such as purchasing quantities that must be integers.
[0071] The optimal spare parts procurement strategy refers to the final solution that meets all constraints (covering extreme failure requirements and meeting risk thresholds) and has the lowest total procurement cost, and clarifies the specific procurement quantity, specifications, and other decision information for various spare parts.
[0072] The processing equipment uses a professional mathematical programming solver to select either a linear programming solver or an integer programming solver based on the attributes of the model's decision variables. This allows for the computation and solution of the transformed single-layer optimization model, ultimately yielding a spare parts procurement plan that satisfies all constraints and has the optimal total procurement cost.
[0073] The specific implementation logic is as follows: First, select the appropriate solver based on the characteristics of the single-layer optimization model. If the spare parts procurement quantity is allowed to be non-integer (theoretical calculation scenario), then the linear programming solver is selected; if the procurement quantity needs to be an integer (actual procurement scenario), then the integer programming solver is selected.
[0074] When the solver runs, it will automatically traverse all combinations of spare parts procurement quantities that meet the constraints. Under the premise of covering all boundary scenario failure requirements and meeting probability and quantity constraints, it will accurately locate the combination with the lowest total procurement cost. This combination is the optimal spare parts procurement strategy.
[0075] This process replaces complex manual calculations, ensuring the accuracy of the solutions and significantly improving decision-making efficiency, thus ensuring that optimization strategies can be quickly implemented and applied.
[0076] The method also includes: The processing equipment generates a procurement list and execution plan based on the optimal spare parts procurement strategy; and completes the actual procurement and inventory configuration of spare parts according to the procurement list and execution plan.
[0077] The procurement list is a structured procurement document generated based on the optimal spare parts procurement strategy. It clearly records information such as the procurement quantity, specifications, and corresponding component matching relationships of various spare parts, and serves as the basis for the actual procurement.
[0078] The execution plan is a practical implementation plan developed around the procurement list. It includes procurement timelines, supplier selection priorities, phased procurement arrangements, and warehousing and acceptance standards, and is used to standardize the entire procurement process.
[0079] Actual procurement is the specific operation by which power companies purchase spare parts from suppliers through bidding, designated procurement, and other methods based on the procurement list and execution plan. It is a key link in transforming optimization strategies into physical inventory.
[0080] Inventory configuration involves warehousing and allocation planning of spare parts after they are procured and put into storage, according to preset rules (such as classifying and storing them according to component deployment area, failure frequency, and maintenance convenience) to ensure that spare parts can be quickly retrieved when needed.
[0081] The processing equipment first transforms the abstract optimal spare parts procurement strategy into a directly executable procurement list and execution plan. Then, through the actual procurement and inventory configuration, it completes a closed loop from strategy optimization to material support, ultimately achieving lean management of spare parts procurement and inventory.
[0082] The specific implementation logic is as follows: First, the equipment extracts information such as the quantity and specifications of various spare parts from the optimal procurement strategy to generate a well-organized procurement list. At the same time, it formulates a clear execution plan based on the actual situation such as the power grid operation and maintenance rhythm and the supplier's delivery cycle, such as purchasing high-frequency fault spare parts in batches on a quarterly basis and prioritizing suppliers with stable supply. Second, the procurement department initiates the actual procurement process based on the procurement list and execution plan, and completes operations such as contract signing, payment, and spare parts acceptance. Third, the accepted spare parts are stored in the warehouse according to the inventory configuration rules. For example, spare parts for core components are centrally stored in the regional operation and maintenance center, and commonly used spare parts are deployed in the warehouses of each substation. At the same time, the inventory management system data is updated synchronously to ensure that the inventory status is available in real time.
[0083] This process streamlines the entire process from strategy optimization, planning, procurement execution, and inventory implementation. It ensures the effective implementation of the optimal procurement strategy and improves the efficiency of spare parts utilization through standardized lists, plans, and inventory management, providing solid material support for rapid response to power grid maintenance work.
[0084] Based on the above description, this application has the following beneficial effects: First, by acquiring component operation data and spare parts unit procurement costs, and combining probability constraints and quantity constraints, a two-layer robust optimization model with a clear objective is constructed. This model aims to minimize the total procurement cost while fully considering the randomness of power grid component failures, thus breaking the idealized assumptions of demand distribution in traditional methods.
[0085] Secondly, a boundary scenario generation algorithm based on component-by-component testing is adopted to traverse all reasonable fault combinations and filter out the set of boundary fault scenarios, ensuring that the model can cover extreme but feasible fault situations. This significantly improves the robustness of the procurement strategy to uncertain faults and solves the problem that existing methods are unable to cope with sudden faults.
[0086] Furthermore, by constructing operational constraints that ensure the procurement quantity is not less than the scenario failure requirements and transforming them into a single-layer optimization model, and using a mature mathematical programming solver to achieve efficient solution, the accuracy of decision-making is guaranteed, while avoiding the problem of low solution efficiency of complex models.
[0087] Finally, by generating procurement lists and execution plans, a closed-loop implementation from strategy optimization to actual procurement and inventory allocation was achieved. This not only ensured the rapid response and reliable execution of power system maintenance work, contributing to the safe and stable operation of the power grid, but also avoided cost waste caused by blind procurement or spare parts shortages by accurately controlling the procurement quantity, providing strong support for the lean operation of power companies.
[0088] The above text combined Figure 1 The method for optimizing spare parts procurement for power companies provided in the embodiments of this application has been described in detail. The apparatus and equipment provided in the embodiments of this application will be described below with reference to the accompanying drawings.
[0089] like Figure 2 As shown in the figure, this is a schematic diagram of an apparatus for optimizing spare parts procurement in power companies according to an embodiment of this application. The apparatus includes: The acquisition module 301 is used to acquire the operating data of various components in the power grid and the unit procurement cost of spare parts; The processing module 302 is used to establish a two-layer robust optimization model for spare parts procurement with the objective of minimizing the total procurement cost, based on the running data and the unit procurement cost of spare parts, under the constraints of component failure scenarios; generate a set of boundary failure scenarios that satisfy the constraints through a boundary scenario generation algorithm; construct running constraints based on the set of boundary failure scenarios, and add the running constraints to the initial optimization model to form a single-layer optimization model equivalent to the two-layer robust optimization model; The determination module 303 is used to obtain the optimal spare parts procurement strategy by solving the single-layer optimization model.
[0090] Optionally, the processing module 302 is specifically used to start testing from a single component failure scenario; gradually increase the number of components that fail simultaneously to generate new test scenarios; stop expanding when the test scenario does not meet the constraints, and add the last scenario that meets the constraints to the boundary failure scenario set; traverse all possible component combinations to generate the final boundary failure scenario set.
[0091] Optionally, the processing module 302 is specifically used to construct an operational constraint for each scenario in the set of boundary fault scenarios, for any spare part type g, that the procurement quantity of spare part type g is greater than or equal to the sum of the number of component failures belonging to spare part type g in the corresponding scenario.
[0092] Optionally, the processing module 302 is specifically used to solve the single-layer optimization model using a mathematical programming solver, which includes a linear programming solver or an integer programming solver.
[0093] Optionally, the determining module 303 is further configured to generate a procurement list and an execution plan based on the optimal spare parts procurement strategy; and to complete the actual procurement and inventory configuration of spare parts according to the procurement list and the execution plan.
[0094] The apparatus for optimizing spare parts procurement for power companies according to the embodiments of this application can correspond to the execution of the method described in the embodiments of this application, and the other operations and / or functions of each module / unit of the apparatus for optimizing spare parts procurement for power companies are respectively for the purpose of implementing Figure 1 For the sake of brevity, the corresponding processes of each method in the illustrated embodiments will not be described in detail here.
[0095] This application also provides a computing device. For example... Figure 3 As shown in the figure, this is a schematic diagram of a computing device provided in an embodiment of this application. The computing device 700 includes a bus 701, a processor 702, a communication interface 703, and a memory 704. The processor 702, the memory 704, and the communication interface 703 communicate with each other via the bus 701.
[0096] The 701 bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.
[0097] The processor 702 can be any one or more of the following processors: central processing unit (CPU), graphics processing unit (GPU), microprocessor (MP), or digital signal processor (DSP).
[0098] The communication interface 703 is used for communication with external devices.
[0099] Memory 704 may include volatile memory, such as random access memory (RAM). Memory 704 may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).
[0100] The memory 704 stores executable code, and the processor 702 executes the executable code to perform the aforementioned method for optimizing the procurement of spare parts for power companies.
[0101] Specifically, in achieving Figure 2 In the case of the illustrated embodiment, and Figure 2 When the modules or units of the device for optimizing spare parts procurement for power companies described in the embodiments are implemented through software, the execution... Figure 2 The software or program code required for the functions of each module / unit can be partially or entirely stored in memory 704. Processor 702 executes the program code corresponding to each unit stored in memory 704, and executes the aforementioned method for optimizing the procurement of spare parts for power companies.
[0102] This application also provides a computer-readable storage medium. The computer-readable storage medium can be any available medium that a computing device can store, or a data storage device such as a data center containing one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives). The computer-readable storage medium includes instructions that instruct the computing device to execute the above-described method for optimizing spare parts procurement for power companies.
[0103] This application also provides a computer program product comprising one or more computer instructions. When the computer instructions are loaded and executed on a computing device, all or part of the processes or functions described in this application are generated.
[0104] The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, or data center to another website, computer, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means.
[0105] When the computer program product is executed by a computer, the computer performs any of the aforementioned methods for optimizing the procurement of spare parts for power enterprises. The computer program product can be a software installation package; when any of the aforementioned methods for optimizing the procurement of spare parts for power enterprises needs to be used, the computer program product can be downloaded and executed on the computer.
[0106] The descriptions of the processes or structures corresponding to the above figures each have their own emphasis. For parts of a process or structure that are not described in detail, please refer to the relevant descriptions of other processes or structures.
[0107] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be covered within the scope of protection of this application.
Claims
1. A method for optimizing spare parts procurement in power companies, characterized in that, The method includes: Obtain operational data and unit procurement costs of various components in the power grid; Based on the aforementioned operational data and spare parts unit procurement costs, a two-layer robust optimization model for spare parts procurement is established under the constraints of component failure scenarios, with the objective of minimizing total procurement costs. A set of boundary fault scenarios that satisfy the aforementioned constraints is generated using a boundary scenario generation algorithm. Based on the set of boundary fault scenarios, operational constraints are constructed and added to the initial optimization model to form a single-layer optimization model equivalent to the two-layer robust optimization model. The optimal spare parts procurement strategy is obtained by solving the single-layer optimization model.
2. The method according to claim 1, characterized in that, The step of generating a set of boundary fault scenarios that satisfy the constraints using a boundary scenario generation algorithm includes: Start testing with a single component failure scenario; Gradually increase the number of components that fail simultaneously to generate new test scenarios; When the test scenario does not meet the constraints, the expansion stops and the last scenario that meets the constraints is added to the boundary fault scenario set. Iterate through all possible combinations of components to generate the final set of boundary fault scenarios.
3. The method according to claim 1, characterized in that, The construction of operational constraints based on the set of boundary fault scenarios includes: For each scenario in the set of boundary fault scenarios, for any spare part type g, construct an operational constraint that the procurement quantity of spare part type g is greater than or equal to the sum of the number of faults of all components belonging to spare part type g in the corresponding scenario.
4. The method according to claim 1, characterized in that, The solution to the single-layer optimization model is accomplished using a mathematical programming solver, which includes a linear programming solver or an integer programming solver.
5. The method according to claim 1, characterized in that, The constraints of the component failure scenario include probability constraints and quantity constraints; The probability constraint is that the joint probability of multiple faults occurring simultaneously is less than or equal to the probability threshold. The quantity constraint is defined as the total number of components that fail within the same time period being less than or equal to a quantity threshold.
6. The method according to claim 1, characterized in that, The operational data includes component types, operational quantities, and failure probability distribution.
7. The method according to claim 1, characterized in that, The method further includes: Based on the optimal spare parts procurement strategy, a procurement list and execution plan are generated; Based on the aforementioned procurement list and execution plan, complete the actual procurement and inventory configuration of spare parts.
8. A device for optimizing spare parts procurement in power companies, characterized in that, The device includes: The acquisition module is used to acquire operating data of various components in the power grid and the unit procurement cost of spare parts; The processing module is used to establish a two-layer robust optimization model for spare parts procurement with the objective of minimizing the total procurement cost, based on the operational data and the unit procurement cost of spare parts, under the constraints of component failure scenarios; generate a set of boundary failure scenarios that satisfy the constraints through a boundary scenario generation algorithm; construct operational constraints based on the set of boundary failure scenarios, and add the operational constraints to the initial optimization model to form a single-layer optimization model equivalent to the two-layer robust optimization model; The determination module is used to obtain the optimal spare parts procurement strategy by solving the single-layer optimization model.
9. A computing device, characterized in that, Including memory and processor; The memory stores one or more computer programs, the one or more computer programs including instructions; when the instructions are executed by the processor, the computing device performs the method as described in any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store a computer program for performing the method as described in any one of claims 1 to 7.