Thermal power plant fuel purchase plan calculation method and device and storage medium

Abstract

The invention discloses a thermal power plant fuel purchase plan calculation method and device and a storage medium. The method comprises the steps that fuel data, related to coal consumption requirements, inventory requirements, price fluctuation and unloading operation, of a thermal power plant are acquired; based on the fuel data, a mathematical planning model with the lowest total cost of a planned purchase order, the minimum queuing time of ship arrival operation and the lowest fuel inventory as optimization objectives is constructed, and constraint conditions of the mathematical planning model include order arrival operation time constraint, coal type inventory change constraint and coal type safety inventory constraint; at least one of an order purchase quantity upper limit constraint and a coal yard inventory upper limit constraint; and calling an optimization solver to solve the mathematical programming model, wherein an obtained optimal solution result is a thermal power plant fuel purchase plan. According to the invention, the procurement planning scheme has the optimality of data theory guarantee, and the procurement and inventory management cost is optimized to the maximum extent while the normal operation of the power plant is guaranteed.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of coal-fired power plants, and in particular to a method, device, equipment and storage medium for calculating a fuel procurement plan for a thermal power plant. Background Art

[0002] As an important component of basic energy, coal is a key support for promoting rapid economic development, and coal-fired power generation is still one of the main ways of power generation. However, due to the uneven quality of coal used by coal-fired power plants and limited high-quality coal resources, power plants generally use a mixture of high-quality coal and other types of coal. If the amount of high-quality coal used is too small, it cannot meet the power generation requirements; and excessive use will lead to rising costs. Therefore, the supply of low-priced high-quality coal often cannot meet demand. For this reason, coal-fired power plants usually adopt the method of purchasing high-quality coal and imported economical coal at the same time to reduce operating costs.

[0003] Under the premise of daily power generation demand, how to ensure safety inventory and reduce inventory and procurement costs is still an important operational challenge facing coal-fired power plants. At present, the procurement plans of most coal-fired power plants still rely on manual experience, and the management mode is relatively extensive and short-term. Although the formulated procurement plans can basically meet normal operations, problems such as excess fuel inventory and queues for unloading operations often occur. At the same time, it is difficult to flexibly adjust the procurement plan according to the actual operation of the power plant. This leads to power plants often facing problems such as high coal yard management costs, untimely replenishment of high-quality coal, and insufficient coal yard space.

[0004] Existing research mainly focuses on ensuring that the total amount of fuel in the next few months can meet demand and keep the daily inventory above the safety line. However, there is relatively little research on how to dynamically adjust the purchase volume and purchase time according to operating conditions and changes in coal prices to reduce inventory accumulation costs. In order to reduce fuel costs, coal-fired power plants urgently need new decision-making methods to optimize fuel procurement plans. Summary of the invention

[0005] The purpose of the present invention is to at least provide a method, device, equipment and storage medium for calculating the fuel procurement plan of a thermal power plant, which can at least solve the technical problem in the prior art that there is no procurement plan that can dynamically adjust the procurement quantity and procurement time according to the operating conditions and changes in coal prices to reduce inventory accumulation costs.

[0006] To solve the above technical problems, at least one embodiment of the present application provides a method for calculating a fuel procurement plan for a thermal power plant, comprising:

[0007] Obtain fuel data for thermal power plants related to coal demand, inventory requirements, price fluctuations, and unloading operations;

[0008] Based on the fuel data related to coal demand, inventory requirements, price fluctuations and unloading operations of the thermal power plant, a mathematical programming model is constructed with the optimization objectives of minimizing the total cost of planned purchase orders, minimizing the queuing time for ship arrival operations and minimizing the fuel inventory. The constraints of the mathematical programming model include at least one of the constraints of order arrival operation time, coal inventory change constraints, coal safety inventory constraints, order purchase quantity upper limit constraints and coal yard inventory upper limit constraints;

[0009] The optimization solver is called to solve the mathematical programming model, and the optimal solution obtained is the fuel procurement plan of the thermal power plant.

[0010] At least one embodiment of the present application further provides a device for calculating a fuel purchase plan for a thermal power plant, comprising:

[0011] The acquisition module is used to obtain fuel data related to coal demand, inventory requirements, price fluctuations and unloading operations of thermal power plants;

[0012] A model building module is used to build a mathematical programming model with the optimization objectives of minimizing the total cost of planned purchase orders, minimizing the queuing time for ship arrival operations, and minimizing the fuel inventory based on the fuel data related to the coal demand, inventory requirements, price fluctuations, and unloading operations of the thermal power plant, wherein the constraints of the mathematical programming model include at least one of the constraints of order arrival operation time, coal inventory change constraints, coal safety inventory constraints, order purchase quantity upper limit constraints, and coal yard inventory upper limit constraints;

[0013] The solution module is used to call the optimization solver to solve the mathematical programming model, and the optimal solution obtained is the fuel procurement plan of the thermal power plant.

[0014] At least one embodiment of the present application also provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the above-mentioned method for calculating the fuel procurement plan for a thermal power plant.

[0015] At least one embodiment of the present application further provides a computer-readable storage medium storing a computer program, which implements the above-mentioned method for calculating the fuel procurement plan of a thermal power plant when executed by a processor.

[0016] The fuel purchase plan calculation method, device, electronic device and computer-readable storage medium provided by the embodiment of the present application are transformed into a certain mathematical programming problem by combining business rules and real-time data of coal-fired power plants from a business perspective, and solving it with the help of an optimal solver with accurate solving ability, so as to ensure that the purchase plan has the optimality guaranteed by data theory. While ensuring the normal operation of the power plant, the purchase and inventory management costs are optimized to the maximum extent. The present application aims to comprehensively consider factors such as coal demand, inventory requirements, price fluctuations and unloading operations, and reduce the purchase and operation costs while ensuring the demand for power generation and heating and maintaining safety inventory. The problem is converted into a quantitative mathematical programming model. Through this conversion, complex business problems are transformed into a process of finding a mathematical optimal solution, so that the optimization scheme has strict mathematical theoretical support, and the optimal solution obtained can not only realize the optimization of the purchase plan, but also quantitatively evaluate it, and maximize the cost-effectiveness under the premise of strictly meeting business constraints. In addition, in the modeling process, only the necessary data and business requirements need to be input, and the experience of special business personnel is not relied on. Therefore, even in the case of new power plants, new equipment, new types of coal, or sudden changes in the operating conditions of power plants due to failures, maintenance, etc., the required procurement plan can still be generated quickly, with good adaptability and the ability to respond to emergencies.

[0017] In some optional embodiments, the fuel data related to coal demand, inventory requirements, price fluctuations and unloading operations of the thermal power plant includes purchase orders for upcoming or planned unloading operations, consumption rates and safety inventories of various types of coal within the planning window, coal storage data of the coal-fired power plant, optional purchase order information data and price index information of various types of coal within the planning window.

[0018] Comprehensive consideration of factors such as the real-time fuel inventory of coal-fired power plants, daily consumption of various fuels, fuel purchase price fluctuations, and safety inventory requirements facilitates the subsequent construction of a mathematical programming model aimed at minimizing operating costs.

[0019] In some optional embodiments, the objective function of the mathematical programming model is expressed as:

[0020] obj=min(ω1·cost+ω2·balance_stock)

[0021]

[0022] Where obj is the objective function; ω1 is the weight of the number of ships on the objective function; ω2 is the weight of the balance of excess inventory on the objective function; balance_stock is the balance of excess inventory; cost s，tChoose the cost of order s arriving at time t; x s，t is the main decision variable, which is used to indicate whether order s chooses to arrive at the port at time t. If so, the value is 1, otherwise it is 0; is the maximum inventory of coal type k in the coal yard at time t; is the minimum inventory of coal type k in the coal yard at time t.

[0023] In some optional embodiments, the order arrival operation time constraint is that no other orders will be arranged to arrive at the port within the order arrival operation time. The expression of the order arrival operation time constraint is:

[0024]

[0025] Among them, x s，t is the main decision variable, which is used to indicate whether order s chooses to arrive at the port at time t. If so, the value is 1, otherwise it is 0; t0 is the initial time of the order at the port; u port is the arrival time of the order at the port; S is the order set; T is the optimized time set.

[0026] In some optional embodiments, the coal inventory change constraint is the sum of the inventory of the coal type on the previous day minus the coal type consumption on the previous day and the arrival quantity of the coal type on the current day. The expression of the coal inventory change constraint is:

[0027]

[0028] Among them, v k，t is the inventory of coal type k in the coal yard at time t; v k，t-1 is the inventory of coal type k in the coal yard at time t-1 day; reduce k，t-1 is the coal consumption of coal type k in the coal yard at time t-1 day; x s，t is the main decision variable, which is used to indicate whether order s chooses to arrive at the port at time t. If yes, the value is 1, otherwise it is 0; amount s is the amount of coal transported for order s; T is the optimized time set; K is the coal type set; S is the order set.

[0029] In some optional embodiments, the coal type safety inventory constraint is that the inventory of all types of coal types is higher than the safety inventory within the optimization time period, and the expression of the coal type safety inventory constraint is:

[0030]

[0031] Among them, v k，t is the inventory of coal type k in the coal yard at time t; is the minimum inventory of coal type k in the coal yard, that is, the safety inventory; T is the optimization time set; K is the coal type set.

[0032] In some optional embodiments, the upper limit constraint of the coal yard inventory is that within the optimization time range, the sum of the inventory of all types of coal does not exceed the maximum inventory of the coal yard. The expression of the upper limit constraint of the coal yard inventory is:

[0033]

[0034] Among them, v k，t is the inventory of coal type k in the coal yard at time t; StockAmount is the upper limit of coal inventory in the coal yard; T is the optimization time set; K is the coal type set. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] One or more embodiments are exemplarily described by the pictures in the corresponding drawings, and these exemplary descriptions do not constitute limitations on the embodiments.

[0036] Figure 1 is a flow chart of a method for calculating a fuel purchase plan for a thermal power plant provided by an embodiment of the present application;

[0037] Figure 2 is a flow chart of a method for calculating a fuel purchase plan for a thermal power plant provided by another embodiment of the present application;

[0038] Figure 3 is a flow chart of a method for calculating a fuel purchase plan for a thermal power plant provided by another embodiment of the present application;

[0039] Figure 4 is a schematic diagram of a fuel purchase plan calculation device for a thermal power plant provided by another embodiment of the present application;

[0040] Figure 5 It is a schematic diagram of the structure of an electronic device provided by another embodiment of the present application.

[0041] In the drawings, the same reference numerals are used for the same components, and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0042] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings. However, it will be appreciated by those skilled in the art that in the present application, many technical details are proposed in order to enable the reader to better understand the present application. However, even without these technical details and various changes and modifications based on the following embodiments, the technical scheme claimed in the present application can also be implemented. The division of the following embodiments is for the convenience of description, and the specific implementation of the present application should not be construed as any limitation, and the various embodiments can be combined and referenced with each other under the premise of no contradiction.

[0043] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0044] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0045] The present invention proposes a method for calculating a fuel procurement plan for a thermal power plant. The implementation details of the method for calculating a fuel procurement plan for a thermal power plant in this embodiment are specifically described below. The following content is only provided for the convenience of understanding the implementation details and is not necessary for implementing this solution.

[0046] Embodiment 1:

[0047] The specific process of the method for calculating the fuel purchase plan for a thermal power plant in this embodiment can be as follows: Figure 1 As shown, including:

[0048] Step 110, obtaining fuel data related to coal demand, inventory requirements, price fluctuations, and unloading operations of the thermal power plant.

[0049] Specifically, obtain fuel data related to coal demand, inventory requirements, price fluctuations and unloading operations of thermal power plants, including real-time fuel inventory of thermal power plants, daily consumption of various types of fuels, fuel purchase price index information and safety stock requirements, purchase orders for upcoming or planned unloading operations, etc.

[0050] Step 120, based on the fuel data related to the coal demand, inventory requirements, price fluctuations and unloading operations of the thermal power plant, a mathematical programming model is constructed with the optimization objectives of minimizing the total cost of planned purchase orders, minimizing the waiting time for ship arrival operations and minimizing the fuel inventory. The constraints of the mathematical programming model include at least one of the constraints on the order arrival operation time, the coal type inventory change constraints, the coal type safety inventory constraints, the order purchase quantity upper limit constraints and the coal yard inventory upper limit constraints.

[0051] Specifically, based on the acquired fuel data, the fuel purchase plan of the thermal power plant is organized into a mixed integer programming problem including optimization objectives, constraints and decision variables, namely, a mathematical programming model, wherein the optimization objectives of the mathematical programming model are to minimize the total cost of the planned purchase order, minimize the queuing time for the ship to the port, and minimize the fuel inventory, namely, to minimize the total cost of the planned purchase order, minimize the queuing time for the ship to the port, and minimize the fuel inventory. The constraints of the mathematical programming model include at least one of the constraints of the order arrival time, the coal inventory change constraints, the coal safety inventory constraints, the order purchase quantity upper limit constraints, and the coal yard inventory upper limit constraints.

[0052] Step 130, calling an optimization solver to solve the mathematical programming model, and the optimal solution obtained is the fuel procurement plan of the thermal power plant.

[0053] Specifically, a solver is a computational program or tool used to solve various mathematical, engineering or optimization problems. By selecting a suitable solver and configuring parameters, the constructed mathematical programming model can be solved to obtain the fuel procurement plan for the thermal power plant.

[0054] In some examples, the mathematical programming model is solved by the COPT solver, and the optimal solution obtained is the fuel procurement plan of the thermal power plant. Among them, the COPT solver is an optimization software specially designed for solving mathematical programming problems, providing standardized analysis methods. By supporting mathematical programming and constraint programming, users can quickly develop and apply decision optimization models, and transform complex business challenges into mathematical programming models. Efficient optimization algorithms help to quickly find the optimal solution of these models. Specifically, considering the real-time fuel inventory of coal-fired power plants, daily consumption of various fuels, fuel procurement price fluctuations, and safety inventory requirements, a mathematical programming model with the goal of minimizing operating costs is constructed. Through operations research optimization technology and using the COPT solver to solve the model, the optimal solution to the combinatorial optimization problem is obtained, thereby determining the final procurement plan.

[0055] In some embodiments, the fuel data related to coal demand, inventory requirements, price fluctuations and unloading operations of the thermal power plant includes purchase orders for upcoming or planned unloading operations, consumption rates and safety inventories of various types of coal within the planning window, coal storage data of the coal-fired power plant, optional purchase order information data and price index information of various types of coal within the planning window.

[0056] Specifically, the fuel data related to coal demand, inventory requirements, price fluctuations and unloading operations of thermal power plants include purchase orders for unloading operations that are about to or are planned to be carried out, among which the date of arrival of the ship and the time of the unloading operation need to be considered; as well as the consumption rate and safety inventory of various types of coal within the planning window, among which it is necessary to consider whether the unit is planned to operate, the daily planned power generation, and the consumption ratio of fuels of different calorific values; as well as the coal storage data of coal-fired power plants, including the inventory of all types of coal, the source and calorific value type of coal, and whether it is an economical type of coal; as well as optional purchase order information data, including order coal type information, planned arrival date range, order quantity, purchase price, order type, and it is necessary to consider whether the order type is required, optional, or multiple-choice; as well as price index information of coal types of different source types and calorific value types within the planning window period.

[0057] In some embodiments, the objective function of the mathematical programming model is expressed as:

[0058] obj=min(ω1·cost+ω2·balance_stock)

[0059]

[0060] Where obj is the objective function; ω1 is the weight of the number of ships on the objective function; ω2 is the weight of the balance of excess inventory on the objective function; balance_stock is the balance of excess inventory; cost s，t Choose the cost of order s arriving at time t; x s，t is the main decision variable, which is used to indicate whether order s chooses to arrive at the port at time t. If so, the value is 1, otherwise it is 0; is the maximum inventory of coal type k in the coal yard at time t; is the minimum inventory of coal type k in the coal yard at time t.

[0061] Specifically, after collecting the required data, the fuel procurement plan decision problem of a thermal power plant is organized into a mixed integer programming problem that includes optimization objectives, constraints, and decision variables.

[0062] The optimization objectives include:

[0063] Plan the total cost of purchase orders to be as low as possible;

[0064] The waiting time for ships to arrive at the port is as short as possible;

[0065] Keep fuel inventories as low as possible.

[0066] The decision variables include:

[0067] A 0-1 variable related to the order and arrival time, indicating whether the order chooses to arrive at the port at time t. If it chooses to join, the value is 1, otherwise it is 0;

[0068] A continuous variable related to the inventory of coal type and time, indicating the inventory of coal type at time t, and taking the value as a positive real number.

[0069] The constraints include at least one of the following constraints:

[0070] Order arrival time constraint: No other orders will be arranged to arrive at the port during the order arrival time.

[0071] Constraints on coal inventory changes: the sum of the inventory of the coal type on the previous day minus the consumption of the coal type on the previous day and the arrival volume of the coal type on that day.

[0072] Coal safety inventory constraint: The inventory of all types of coal is higher than the safety inventory during the optimization time period.

[0073] Order purchase quantity upper limit constraint: During the optimization period, the planned purchase order quantity must not exceed the upper limit.

[0074] Coal yard inventory upper limit constraint: During the optimization time period, the sum of the inventory of all types of coal shall not exceed the maximum inventory of the coal yard.

[0075] Among them, the parameters and sets required for modeling are determined by the acquired data as follows:

[0076] Among them, Table 1 shows the sets required for modeling and their corresponding explanations.

[0077] Table 1. The sets required for modeling and their corresponding explanations

[0078]

[0079] Among them, Table 2 shows the parameters required for modeling and their corresponding explanations.

[0080] Table 2 The sets required for modeling and their corresponding explanations

[0081]

[0082]

[0083] The decision variables for modeling obtained from the data are described in Table 3 below:

[0084] Table 3 Modeling decision variables and their corresponding variable types and explanations

[0085]

[0086] The objective function of the mathematical programming model is expressed as:

[0087] obj=min(ω1·cost+ω2·balance_stock)

[0088] Where ω1 and ω2 represent the weights of the balance between the number of ships and the excess inventory on the objective function. Specifically, each part of the expression is

[0089]

[0090] Where obj is the objective function; ω1 is the weight of the number of ships on the objective function; ω2 is the weight of the balance of excess inventory on the objective function; balance_stock is the balance of excess inventory; cost s，t Choose the cost of order s arriving at time t; x s，t is the main decision variable, which is used to indicate whether order s chooses to arrive at the port at time t. If so, the value is 1, otherwise it is 0; is the maximum inventory of coal type k in the coal yard at time t; is the minimum inventory of coal type k in the coal yard at time t.

[0091] In some embodiments, the order arrival operation time constraint is that no other orders will be arranged to arrive at the port within the order arrival operation time. The expression of the order arrival operation time constraint is:

[0092]

[0093] Among them, x s，t is the main decision variable, which is used to indicate whether order s chooses to arrive at the port at time t. If so, the value is 1, otherwise it is 0; t0 is the initial time of the order at the port; u port is the arrival time of the order at the port; S is the order set; T is the optimized time set.

[0094] In some embodiments, the coal inventory change constraint is the sum of the coal inventory on the previous day minus the coal consumption on the previous day and the coal arrival quantity on the current day. The coal inventory change constraint is expressed as:

[0095]

[0096] Among them, v k,t is the inventory of coal type k in the coal yard at time t; v k，t-1 is the inventory of coal type k in the coal yard at time t-1 day; reduce k，t-1 is the coal consumption of coal type k in the coal yard at time t-1 day; x s，t is the main decision variable, which is used to indicate whether order s chooses to arrive at the port at time t. If yes, the value is 1, otherwise it is 0; amount s is the amount of coal transported for order s; T is the optimized time set; K is the coal type set; S is the order set.

[0097] In some embodiments, the coal safety inventory constraint is that the inventory of all types of coal is higher than the safety inventory within the optimization time period, and the expression of the coal safety inventory constraint is:

[0098]

[0099] Among them, v k，t is the inventory of coal type k in the coal yard at time t; is the minimum inventory of coal type k in the coal yard, that is, the safety inventory; T is the optimization time set; K is the coal type set.

[0100] In some embodiments, the upper limit constraint of the coal yard inventory is that within the optimization time range, the sum of the inventory of all types of coal does not exceed the maximum inventory of the coal yard. The expression of the upper limit constraint of the coal yard inventory is:

[0101]

[0102] Among them, v k，t is the inventory of coal type k in the coal yard at time t; StockAmount is the upper limit of coal inventory in the coal yard; T is the optimization time set; K is the coal type set.

[0103] In some embodiments, the order purchase upper limit constraint is that the number of orders planned for purchase must not exceed the upper limit within the optimization time period. The expression of the order purchase upper limit constraint is:

[0104]

[0105] Among them, v k，t is the inventory of coal type k in the coal yard at time t; U is the set of coal source types; S is the order set; T is the optimization time set.

[0106] Limit usage based on order type

[0107] For any order s:

[0108] If order s is a fixed order (there is a fixed ship arrival plan, etc.), the arrival time of the order is strictly executed according to the fixed information. To show the discussion of all situations, the fixed order is given in the document in the form of constraints. In actual development, when the problem scale is large, the fixed order should not create corresponding variables, but should be directly modified in the port time window, coal yard inventory and other data affected by the fixed order, or add auxiliary variables for modification.

[0109]

[0110] If order s is an optional order, the order needs to arrive at most once within the optional time window.

[0111]

[0112] If order s is a mandatory order, the order must arrive exactly once within the optional time window.

[0113]

[0114] If order s is a multi-select order, the order needs to arrive at least once within the selectable time window.

[0115]

[0116] Embodiment 2:

[0117] Another embodiment of the present application relates to a method for calculating a fuel purchase plan for a thermal power plant based on an operations research method and a combinatorial optimization method. The specific process can be as follows: Figure 2 As shown, including:

[0118] Step (1): Collect the data required for decision making and integrate it into the database used by the power plant's information system.

[0119] Specifically, the main data that needs to be collected include:

[0120] For purchase orders that are about to or are planned to carry out unloading operations, the date of the ship's arrival at the port and the time of the unloading operation need to be considered;

[0121] The consumption rate and safety stock of coal of different calorific value types during the planning window period need to take into account whether the unit is planned to operate, the daily planned power generation, and the consumption ratio of fuel of different calorific value types;

[0122] Coal inventory data of coal-fired power plants, including the inventory of all types of coal, the source and calorific value of coal, and whether it is economical coal;

[0123] Optional purchase order information data, including order coal type information, planned arrival date range, order quantity, purchase price, and order type. It is necessary to consider whether the order type is required, optional, or multiple choice.

[0124] Price index information of coal types of different sources and calorific value types during the planning window period.

[0125] Step (2): Organize the procurement plan decision problem into a mixed integer programming problem that includes optimization objectives, constraints, and decision variables.

[0126] Specifically, after collecting the required data, the procurement plan decision problem is organized into a mixed integer programming problem that includes optimization objectives, constraints, and decision variables, so that it can be solved using mathematical programming solving software in subsequent steps.

[0127] The optimization objective is:

[0128] Plan the total cost of purchase orders to be as low as possible;

[0129] The waiting time for ships to arrive at the port is as short as possible;

[0130] Keep fuel inventories as low as possible.

[0131] The decision variables are:

[0132] A 0-1 variable related to the order and arrival time, indicating whether the order chooses to arrive at the port at time t. If it chooses to join, the value is 1, otherwise it is 0;

[0133] A continuous variable related to the inventory of coal type and time, indicating the inventory of coal type at time t, and taking the value as a positive real number.

[0134] The constraints are:

[0135] Safety stock constraint: The inventory of each type of coal must be higher than the safety stock within the optimization time window;

[0136] Order purchase quantity upper limit constraint: the order quantity planned for purchase within the optimization period shall not exceed the upper limit;

[0137] Coal yard inventory upper limit constraint: the total inventory of coal types within the optimization time period shall not exceed the inventory upper limit.

[0138] Step (3): Use mathematical programming software to solve the problem.

[0139] Specifically, the model is solved using a solver to obtain the optimal solution to the combinatorial optimization problem, thereby determining the final procurement plan.

[0140] In some examples, COPT solver is used for solving. COPT solver is an optimization software designed for solving mathematical programming problems, providing standardized analysis methods. By supporting mathematical programming and constraint programming, users can quickly develop and apply decision optimization models and transform complex business challenges into mathematical programming models. Efficient optimization algorithms help quickly find the optimal solutions to these models.

[0141] In this embodiment, the above-mentioned mixed integer programming problem is first constructed as a mathematical programming model that can be recognized by the solver and converted through a computer language. Subsequently, a commercial solver is used to solve the mathematical programming problem. A commercial solver is a professional software that encapsulates a variety of algorithms and techniques, and can flexibly call suitable algorithms to solve problems based on the mathematical structure and data characteristics of the problem. The mathematical solution given by the solver is to achieve the optimal procurement plan for the business indicators represented by the objective function under the premise of satisfying all constraints. Finally, this mathematical solution is converted into an output form that is easy for business personnel to understand, and this method can provide a specific implementation plan that can be directly used to guide the procurement plan.

[0142] In this embodiment, a mixed integer programming model of actual coal inventory, power generation and heating plans, fuel price fluctuations, coal use rules and other business information of coal-fired power plants is constructed, and the COPT solver is used to solve this combinatorial optimization problem. The mathematical optimal solution of the model corresponds to an optimized procurement plan, and it is directly applied to the fuel procurement of the power plant. This embodiment effectively solves the problems of time-consuming, high cost, and poor flexibility in dealing with special situations when traditional manual procurement plans are formulated. That is, this embodiment takes the procurement plan formulation problem from a business perspective, and models it by combining business rules and real-time data of coal-fired power plants, converting it into a definite mathematical programming problem, and solving it with the help of a commercial solver with accurate solving capabilities, thereby ensuring that the procurement plan has the optimality guaranteed by mathematical theory. While ensuring the normal operation of the power plant, the procurement and inventory management costs are optimized to the maximum extent. In addition, in the modeling process, this embodiment only needs to input the necessary data and business requirements, and does not rely on the experience of specific business personnel. Therefore, even in the case of a new power plant, new equipment, new type of coal, or a sudden change in the operating status of the power plant due to failure, maintenance, etc., this embodiment can still quickly generate the required procurement plan and has good adaptability and the ability to respond to emergencies.

[0143] Embodiment three:

[0144] Another embodiment of the present application relates to a method for calculating a fuel purchase plan for a thermal power plant, and the specific process can be as follows: Figure 3 As shown, including:

[0145] Step 11: Obtain data from the information system of the coal-fired power plant and import the data into the algorithm module through the database of the system.

[0146] Step 12: Based on the acquired data, construct the mixed integer programming model required for procurement planning decisions, and fill the data as parameters of the objectives, variables, and constraints in the model.

[0147] Step 13: Call the COPT solver to solve the mathematical model established in step 2, sort, process and visualize the obtained mathematical optimal solution, and output it as the final procurement plan.

[0148] The mixed integer programming model involved in the above steps is as follows:

[0149] The parameters and sets required for modeling obtained from the data are described as follows, wherein Table 1 is the sets required for modeling and their corresponding explanations.

[0150] Table 1. The sets required for modeling and their corresponding explanations

[0151]

[0152]

[0153] Among them, Table 2 shows the parameters required for modeling and their corresponding explanations.

[0154] Table 2 The sets required for modeling and their corresponding explanations

[0155]

[0156] The decision variables for modeling obtained from the data are described in Table 3 below:

[0157] Table 3 Modeling decision variables and their corresponding variable types and explanations

[0158]

[0159]

[0160] The objective function of the mathematical programming model is expressed as:

[0161] obj=min(ω1·cost+ω2·balance_stock) (1)

[0162] Where ω1 and ω2 represent the weights of the balance between the number of ships and the excess inventory on the objective function. Specifically, each part of the expression is

[0163]

[0164]

[0165] The constraints are as follows:

[0166] Time constraints for order arrival

[0167] During the order arrival time, no other orders will be arranged to arrive at the port.

[0168]

[0169] Constraints on changes in coal inventory

[0170] The inventory of a certain type of coal on the previous day minus the consumption of that type of coal on the previous day, plus the arrival volume of that type of coal on that day, is calculated to obtain the inventory flow balance of that type of coal.

[0171]

[0172] Coal safety stock constraints

[0173] Within the optimization time range, the inventory levels of all coal types are above the safety stock.

[0174]

[0175] Maximum inventory constraints at coal yards

[0176] Within the optimization time range, the sum of the inventory of all types of coal shall not exceed the maximum inventory of the coal yard.

[0177]

[0178] Order purchase limit constraints

[0179]

[0180] Limit usage based on order type

[0181] For any order s:

[0182] If order s is a fixed order (there is a fixed ship arrival plan, etc.), the arrival time of the order is strictly executed according to the fixed information. To show the discussion of all situations, the fixed order is given in the document in the form of constraints. In actual development, when the problem scale is large, the fixed order should not create corresponding variables, but should be directly modified in the port time window, coal yard inventory and other data affected by the fixed order, or add auxiliary variables for modification.

[0183]

[0184] If order s is an optional order, the order needs to arrive at most once within the optional time window.

[0185]

[0186] If order s is a mandatory order, the order must arrive exactly once within the optional time window.

[0187]

[0188] If order s is a multi-select order, the order needs to arrive at least once within the selectable time window.

[0189]

[0190] Embodiment 4:

[0191] Another embodiment of the present application relates to a fuel procurement plan calculation device for a thermal power plant. The implementation details of the fuel procurement plan calculation device for a thermal power plant in this embodiment are specifically described below. The following content is only for the convenience of understanding the implementation details provided, and is not necessary for the implementation of this solution. The schematic diagram of the fuel procurement plan calculation device for a thermal power plant in this embodiment can be as follows Figure 4 As shown, it includes an acquisition module 801, a model building module 802 and a solution module 803.

[0192] The acquisition module 801 is used to acquire fuel data related to coal demand, inventory requirements, price fluctuations and unloading operations of the thermal power plant.

[0193] The model building module 802 is used to build a mathematical programming model with the optimization objectives of minimizing the total cost of planned purchase orders, minimizing the waiting time for ship arrival operations, and minimizing the fuel inventory based on the fuel data related to the coal demand, inventory requirements, price fluctuations and unloading operations of the thermal power plant. The constraints of the mathematical programming model include at least one of the constraints on the order arrival operation time, the coal type inventory change constraints, the coal type safety inventory constraints, the order purchase quantity upper limit constraints and the coal yard inventory upper limit constraints.

[0194] The solution module 803 is used to call the optimization solver to solve the mathematical programming model, and the optimal solution obtained is the fuel procurement plan of the thermal power plant.

[0195] It is worth mentioning that all modules involved in this embodiment are logic modules. In practical applications, a logic unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, in order to highlight the innovative part of this application, this embodiment does not introduce units that are not closely related to solving the technical problems proposed by this application, but this does not mean that there are no other units in this embodiment.

[0196] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can also be implemented in other ways. The device embodiments described above are merely schematic. For example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the devices, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the above-mentioned module, a program segment or a part of a code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0197] It should be noted that in the present disclosure, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element limited by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or device including the element.

[0198] Embodiment five:

[0199] Another embodiment of the present application relates to an electronic device, such as Figure 5 As shown, it includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can implement the above method steps.

[0200] Among them, the memory and the processor are connected in a bus manner, and the bus may include any number of interconnected buses and bridges, and the bus connects various circuits of one or more processors and memories together. The bus can also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface between the bus and the transceiver. The transceiver can be one element or multiple elements, such as multiple receivers and transmitters, providing a unit for communicating with various other devices on a transmission medium. The data processed by the processor is transmitted on a wireless medium via an antenna, and further, the antenna also receives data and transmits the data to the processor.

[0201] The processor is responsible for managing the bus and general processing, and can also provide various functions, including timing, peripheral interfaces, voltage regulation, power management, and other control functions. Memory can be used to store data used by the processor when performing operations.

[0202] The processor may include, but is not limited to, one or more processors or microprocessors, etc. Each processor may be an application specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a controller, a microcontroller, a microprocessor or other electronic components to execute the methods in the above embodiments.

[0203] Embodiment six:

[0204] Another embodiment of the present application relates to a computer-readable storage medium storing a computer program, which implements the above method steps when executed by a processor.

[0205] That is, those skilled in the art can understand that all or part of the steps in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a program, and the program is stored in a storage medium, including a number of instructions to enable a device (which can be a single-chip microcomputer, chip, etc.) or a processor to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as: ROM), random access memory (Random Access Memory, referred to as: RAM), disk or optical disk and other media that can store program codes.

[0206] The computer-readable storage medium may also store at least one computer executable program / instruction, which may be, for example, a computer-readable instruction. The computer-readable storage medium includes, but is not limited to, for example, a volatile memory and / or a non-volatile memory. The volatile memory may include, for example, a random access memory (RAM) and / or a cache memory (cache), etc. The computer-readable storage medium may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. For example, a non-transitory computer-readable storage medium may be connected to a computing device such as a computer, and then, when the computing device runs the computer-readable instructions stored on the computer-readable storage medium, the various methods described above may be performed.

[0207] In addition, the computer device may also include (but not limited to) a data bus, an input / output (I / O) bus, a display, and input / output devices (eg, keyboard, mouse, speaker, etc.), etc.

[0208] The processor may communicate with external devices via an I / O bus via a wired or wireless network.

[0209] Those skilled in the art will appreciate that the above embodiments are specific embodiments for implementing the present application, and in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present application.

Claims

1. A method for calculating a fuel purchase plan for a thermal power plant, characterized in that: include: Obtain fuel data for thermal power plants related to coal demand, inventory requirements, price fluctuations, and unloading operations; Based on the fuel data related to coal demand, inventory requirements, price fluctuations and unloading operations of the thermal power plant, a mathematical programming model is constructed with the optimization objectives of minimizing the total cost of planned purchase orders, minimizing the queuing time for ship arrival operations and minimizing the fuel inventory. The constraints of the mathematical programming model include at least one of the constraints of order arrival operation time, coal inventory change constraints, coal safety inventory constraints, order purchase quantity upper limit constraints and coal yard inventory upper limit constraints; The optimization solver is called to solve the mathematical programming model, and the optimal solution obtained is the fuel procurement plan of the thermal power plant.

2. The method for calculating the fuel purchase plan of a thermal power plant according to claim 1, characterized in that: The fuel data related to coal demand, inventory requirements, price fluctuations and unloading operations of the thermal power plant includes purchase orders for upcoming or planned unloading operations, consumption rates and safety inventories of various types of coal within the planning window, coal storage data of coal-fired power plants, optional purchase order information data and price index information of various types of coal within the planning window.

3. The method for calculating the fuel purchase plan of a thermal power plant according to claim 1, characterized in that: The objective function of the mathematical programming model is expressed as: obj=min(ω1·cost+ω2·balance_stock) Where obj is the objective function; ω1 is the weight of the number of ships on the objective function; ω2 is the weight of the balance of excess inventory on the objective function; balance_stock is the balance of excess inventory; cost s,t Choose the cost of order s arriving at time t; x s,t is the main decision variable, which is used to indicate whether order s chooses to arrive at the port at time t. If so, the value is 1, otherwise it is 0; is the maximum inventory of coal type k in the coal yard at time t; is the minimum inventory of coal type k in the coal yard at time t.

4. The method for calculating the fuel purchase plan of a thermal power plant according to claim 1, characterized in that: The order arrival operation time constraint is that no other orders will be arranged to arrive at the port within the order arrival operation time. The expression of the order arrival operation time constraint is: Among them, x s,t is the main decision variable, which is used to indicate whether order s chooses to arrive at the port at time t. If so, the value is 1, otherwise it is 0; t0 is the initial time of the order at the port; u port is the arrival time of the order at the port; S is the order set; T is the optimized time set.

5. The method for calculating the fuel purchase plan of a thermal power plant according to claim 1, characterized in that: The coal inventory change constraint is the sum of the coal inventory on the previous day minus the coal consumption on the previous day and the coal arrival volume on the current day. The coal inventory change constraint is expressed as: Among them, v k,t is the inventory of coal type k in the coal yard at time t; v k,t-1 is the inventory of coal type k in the coal yard at time t-1 day; reduce k,t-1 is the coal consumption of coal type k in the coal yard at time t-1 day; x s,t is the main decision variable, which is used to indicate whether order s chooses to arrive at the port at time t. If yes, the value is 1, otherwise it is 0; amount s is the amount of coal transported for order s; T is the optimized time set; K is the coal type set; S is the order set.

6. The method for calculating the fuel purchase plan of a thermal power plant according to claim 1, characterized in that: The coal safety inventory constraint is that the inventory of all types of coal is higher than the safety inventory within the optimization time period. The expression of the coal safety inventory constraint is: Among them, v k,t is the inventory of coal type k in the coal yard at time t; is the minimum inventory of coal type k in the coal yard, that is, the safety inventory; T is the optimization time set; K is the coal type set.

7. The method for calculating the fuel purchase plan of a thermal power plant according to claim 1, characterized in that: The upper limit constraint of the coal yard inventory is that within the optimization time range, the sum of the inventory of all types of coal does not exceed the maximum inventory of the coal yard. The expression of the upper limit constraint of the coal yard inventory is: Among them, v k,t is the inventory of coal type k in the coal yard at time t; StockAmount is the upper limit of coal inventory in the coal yard; T is the optimization time set; K is the coal type set.

8. A fuel purchase plan calculation device for a thermal power plant, characterized in that: include: The acquisition module is used to obtain fuel data related to coal demand, inventory requirements, price fluctuations and unloading operations of thermal power plants; A model building module is used to build a mathematical programming model with the optimization objectives of minimizing the total cost of planned purchase orders, minimizing the queuing time for ship arrival operations, and minimizing the fuel inventory based on the fuel data related to the coal demand, inventory requirements, price fluctuations, and unloading operations of the thermal power plant, wherein the constraints of the mathematical programming model include at least one of the constraints of order arrival operation time, coal inventory change constraints, coal safety inventory constraints, order purchase quantity upper limit constraints, and coal yard inventory upper limit constraints; The solution module is used to call the optimization solver to solve the mathematical programming model, and the optimal solution obtained is the fuel procurement plan of the thermal power plant.

9. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for calculating the fuel procurement plan for a thermal power plant as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for calculating a fuel purchase plan for a thermal power plant according to any one of claims 1 to 7 is implemented.

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