Fuel plan optimization method, device, equipment and medium

Abstract

The invention provides a fuel plan optimization method, device and equipment and a medium, and belongs to the technical field of energy supply optimization. The method comprises the steps that based on variable fuel supply quantities of different supply modes of an energy supply plant, with the minimum inventory growth difference rate as the target, a fuel plan optimization scheduling target function is constructed, and constraint conditions of the fuel plan optimization scheduling target function are determined; performing optimization solving on the fuel plan optimization scheduling objective function, and determining the optimal fuel variable supply quantity of different supply modes of the energy supply plant meeting the constraint condition; and the optimal variable fuel supply quantities of the different supply modes of the energy supply plant are sent to the energy supply plant, so that the energy supply plant adjusts a fuel purchase plan according to the optimal variable fuel supply quantities of the different supply modes of the energy supply plant. According to the invention, multi-aspect optimization of inventory, resources, purchase and operation is realized, so that the operation efficiency and economic benefits of the energy supply plant are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy supply optimization, and in particular to a fuel planning optimization method, a fuel planning optimization device, an electronic device and a computer-readable storage medium. Background Art

[0002] In the field of energy supply, fuel management of energy supply plants is crucial to ensuring a stable energy supply and improving operational efficiency. With the continuous growth of energy demand and the increasing complexity of the energy market, energy supply plants face many challenges in fuel planning. For example, how to reasonably control fuel inventory levels, optimize fuel procurement, and ensure effective coordination of different supply methods while meeting energy supply needs directly affects the cost of energy supply plants, the reliability of energy supply, and the overall operational benefits. If the fuel plan is unreasonable, it may lead to fuel inventory backlogs or shortages, increase operating costs, and even affect the stability of energy supply, which will have an adverse impact on social production and life.

[0003] At present, some energy supply plants mainly rely on experience and simple forecasting methods when formulating fuel plans. For example, they make rough estimates based on historical fuel consumption data to determine fuel purchase volume and inventory levels. Some energy supply plants also refer to market price fluctuations and appropriately increase purchase volume when prices are low, but such decisions often lack systematicity and precision. When considering fuel supply methods, they only make arrangements according to conventional supply channels and contractual agreements, and rarely make dynamic adjustments and optimize configurations for different supply methods from the perspective of overall optimization.

[0004] However, the existing fuel planning methods have the following problems: (1) Empirical judgments are easily affected by subjective factors, resulting in poor accuracy and scientificity of fuel planning; (2) Estimation based solely on historical consumption data is difficult to adapt to the dynamic changes in energy demand, the uncertainty of the fuel supply market, and changes in production conditions within the energy supply plant; (3) For fuel inventory management, it is impossible to accurately determine the reasonable inventory level, which can easily lead to excessive inventory costs or supply interruption risks; (4) In terms of fuel supply methods, there is a lack of optimization and integration, and the advantages of different supply methods cannot be fully utilized, which reduces the fuel supply efficiency and thus affects the economic benefits of the energy supply plant and the stability of energy supply.

[0005] In general, the existing fuel planning methods have problems such as strong subjectivity in experience judgment, difficulty in adapting to dynamic changes, inaccurate inventory management, and lack of optimized integration of supply methods, which affects the economic benefits of energy plants and the stability of energy supply. Summary of the invention

[0006] The purpose of the embodiments of the present invention is to provide a fuel planning optimization method, device, equipment and medium to solve the above-mentioned problems.

[0007] In order to achieve the above object, an embodiment of the present invention provides a fuel planning optimization method, comprising: Based on the variable fuel supply of different supply modes of the energy supply plant, the fuel planning optimization scheduling objective function is constructed with the goal of minimizing the inventory growth difference rate, and the constraints of the fuel planning optimization scheduling objective function are determined; The optimal fuel scheduling objective function is solved to determine the optimal variable fuel supply of different supply modes of the energy supply plant that meets the constraints; The optimal variable fuel supply quantities of different supply modes of the energy supply plant are sent to the energy supply plant, so that the energy supply plant adjusts the fuel procurement plan according to the optimal variable fuel supply quantities of different supply modes of the energy supply plant.

[0008] Optionally, the fuel planning optimization scheduling objective function is: ;in, represents the inventory growth variance rate, represents the fuel inventory increment of the i-th energy supply plant, represents the initial fuel inventory of the i-th energy supply plant, Indicates the number of energy supply plants.

[0009] Optionally, the calculation rule for the fuel inventory increment of the energy supply plant is as follows: ;in, represents the fuel inventory increment of the i-th energy supply plant, represents the fuel inventory at the end of the period of the i-th energy supply plant, represents the fuel in transit quantity of the i-th energy supply plant at the end of the period, represents the fuel consumption of the i-th energy supply plant, represents the initial fuel inventory of the i-th energy supply plant, represents the initial fuel in-transit quantity of the i-th energy supply plant, Represents the fuel supply of the i-th energy supply plant.

[0010] Optionally, the calculation rule of the fuel supply of the energy supply plant is as follows: ;in, represents the fuel supply of the ith energy plant, represents the variable fuel supply of the nth supply mode of the i-th energy supply plant, represents the fixed fuel supply of the i-th energy plant.

[0011] Optionally, the constraints of the fuel planning optimization scheduling objective function include: fuel inventory constraints; The fuel inventory constraints are: ;in, represents the initial fuel in-transit quantity of the i-th energy supply plant, represents the fuel in transit quantity of the i-th energy supply plant at the end of the period, Represents the fuel inventory at the end of the i-th energy supply plant.

[0012] Optionally, the constraints of the fuel plan optimization scheduling objective function include: fuel supply constraints; The fuel supply constraints are: ;in, represents the variable fuel supply of the nth supply mode of the i-th energy supply plant, It represents the maximum variable fuel supply of the nth supply mode of the ith energy supply plant.

[0013] Optionally, the procurement plan includes: a variable supply quantity of fuel plan for different supply modes of the energy supply plant; Adjust the fuel procurement plan based on the optimal variable fuel supply for different supply methods of the energy plant, including: For each supply method of the energy plant: When the difference between the optimal variable fuel supply of the energy supply plant and the planned variable fuel supply corresponding to the energy supply plant is greater than a preset threshold, the planned variable fuel supply corresponding to the energy supply plant is corrected to the optimal variable fuel supply of the energy supply plant to update the fuel procurement plan.

[0014] In a second aspect of an embodiment of the present invention, a fuel planning optimization device is provided, comprising: A function building module is used to build a fuel planning optimization scheduling objective function based on the variable fuel supply of different supply modes of the energy supply plant, with the goal of minimizing the inventory growth difference rate, and determine the constraints of the fuel planning optimization scheduling objective function; The optimization solution module is used to optimize and solve the fuel plan optimization scheduling objective function to determine the optimal fuel variable supply quantity of different supply modes of the energy supply plant that meets the constraint conditions; The planning module is used to send the optimal variable fuel supply quantity of different supply modes of the energy supply plant to the energy supply plant, so that the energy supply plant adjusts the fuel procurement plan according to the optimal variable fuel supply quantity of different supply modes of the energy supply plant.

[0015] In a third aspect of the embodiments of the present invention, an electronic device is provided, comprising: a processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and when the machine-readable instructions are executed by the processor, the above-mentioned fuel planning optimization method is executed.

[0016] In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, storing computer instructions, which, when executed on a computer, enable the computer to execute the above-mentioned fuel planning optimization method.

[0017] Beneficial effects of the present invention: (1) Inventory management optimization: By constructing a function with the minimum inventory growth difference rate as the goal, it helps to achieve a stable growth of fuel inventory in energy supply plants. It avoids the increase in management costs and potential supply risks caused by large inventory fluctuations, makes inventory management more scientific and orderly, and improves the utilization efficiency of inventory funds.

[0018] (2) Reasonable allocation of resources: Accurately calculate the variable supply and optimal value of fuel under different supply methods, and reasonably allocate fuel resources according to actual needs. Ensure that each energy supply plant obtains the appropriate amount of fuel under different supply methods, avoid excessive concentration or shortage of resources, and improve the stability and reliability of the overall energy supply.

[0019] (3) Accurate adjustment of procurement plan: Based on the comparison and correction mechanism between the optimal variable fuel supply and the planned variable fuel supply, the fuel procurement plan is made more in line with actual operational needs. This reduces unnecessary procurement costs, avoids fuel backlogs or shortages caused by unreasonable plans, and improves the accuracy and flexibility of procurement plans.

[0020] (4) Improve operational efficiency: Comprehensively consider fuel inventory constraints and supply constraints to ensure the feasibility of the optimization plan in actual operation. This helps energy supply plants to meet production needs while complying with inventory and supply constraints, optimize production and operation processes, and ultimately improve the overall operational efficiency and economic benefits of energy supply plants.

[0021] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention and constitute a part of the specification. Together with the following specific implementations, they are used to explain the embodiments of the present invention, but do not constitute a limitation on the embodiments of the present invention. In the accompanying drawings: Figure 1 is a flow chart of a fuel planning optimization method provided by an embodiment of the present invention; Figure 2 is a schematic diagram of the architecture of a fuel planning optimization model provided by an embodiment of the present invention; Figure 3 It is a structural schematic diagram of a fuel planning optimization device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The specific implementation of the embodiment of the present invention is described in detail below in conjunction with the accompanying drawings. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiment of the present invention, and is not used to limit the embodiment of the present invention.

[0024] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0025] In the description of the embodiments of the present application, the technical terms "first", "second", etc. are only used to distinguish different objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number, specific order or primary and secondary relationship of the indicated technical features. In the description of the embodiments of the present application, the meaning of "multiple" is more than two, unless otherwise clearly and specifically defined.

[0026] Embodiment 1 Please refer to Figure 1 , Figure 1 : is a flow chart of a fuel planning optimization method provided by an embodiment of the present invention, the method comprising the following steps: S100, based on the variable fuel supply of different supply modes of the energy supply plant, with the minimum inventory growth difference rate as the goal, construct the fuel plan optimization scheduling objective function, and determine the constraint conditions of the fuel plan optimization scheduling objective function; It should be noted that the variable fuel supply of different supply modes of the energy supply plant is different from the fixed fuel supply and can be variable, while the fixed fuel supply is fixed.

[0027] In one embodiment, the fuel planning optimization scheduling objective function is: ;in, represents the inventory growth variance rate, represents the fuel inventory increment of the i-th energy supply plant, represents the initial fuel inventory of the i-th energy supply plant, Indicates the number of energy supply plants.

[0028] The objective function of fuel planning optimization scheduling is Figure 2 The fuel planning optimization model in Figure 2 The fuel planning optimization model in is explained as follows: Figure 2 This is a fuel planning optimization model, which is divided into three sections: inventory section, consumption section and supply section. The main modules in each section are divided as follows: (1) Inventory module The main variables characterize the supply and demand characteristics of the energy supply plant. The known variables are the variables that characterize the characteristics of the energy supply plant, which are marked as characteristic variable 1, characteristic variable 2...characteristic variable n (n is a positive integer). For example, for a typical energy supply plant, the key characteristic variables can be the number of available days, the average daily consumption per month, the proportion of storage capacity, the maximum daily consumption, etc. With the key characteristics of the energy supply plant as input, the end-of-period inventory can be optimized and solved. The optimized end-of-period inventory combines the initial situation, the end-of-period inventory, the beginning-of-period inventory, and the overall inventory strategy of the month to optimize the inventory change of the main variable to be solved.

[0029] (2) Consumption module The main variable is the fuel consumption characteristics of the fuel plant. Usually, there are many ways to consume fuel. For example, the coal consumption of power plants includes power generation and heating. Therefore, the corresponding sections are marked as consumption mode 1, consumption mode 2...consumption mode n (n is a positive integer), all of which are known variables. Through the two fuel consumption modes, the fuel consumption can be directly calculated. Usually, the consumption mode and consumption are in a functional relationship and can be directly calculated.

[0030] (3) Supply module The main variables characterize the supply characteristics of the energy plant, including total supply (unknown, intermediate variable) and fixed supply (known). Fixed supply is the fixed supply path of the fuel plant, which is usually determined before planning and is a known variable. In the section, fixed supply 1, fixed supply 2...fixed supply n are used to represent (n is a positive integer).

[0031] In addition, except for the fixed supply method, all other supply methods of the energy supply plant are variable supply methods, which are represented by variable supply 1, variable supply 2...variable supply n in the section (n is a positive integer). Variable supply methods can be divided according to a certain model, for example, according to sales channels, they can be divided into integrated water supply, integrated direct supply, external supply, etc.

[0032] According to the procurement method, there can be different channels for obtaining supply in the module, which are represented by channel 1, channel 2...channel n (n is a positive integer). The main known variables of the supply module mainly include the total supply of various channels, the size of which can be freely set by the user and is usually determined before planning. The total amount of various channels can be divided according to a certain pattern in the module. For example, according to the procurement channel, it can be divided into the total supply of self-produced supply, the total supply of outsourced supply, and the total supply of commissioned procurement. The module is marked as the total amount of channel 1, the total amount of channel 2...the total amount of channel n (n is a positive integer).

[0033] In the consumption section, suppose the energy generated by n consumption modes is x1, x2…x n , the consumption is y, then we can directly use the function relationship f(x1, x2…x n ) calculated.

[0034] Specifically, the calculation rules for the fuel inventory increase of the energy supply plant are as follows: ;in, represents the fuel inventory increment of the i-th energy supply plant, represents the fuel inventory at the end of the period of the i-th energy supply plant, represents the fuel in transit quantity of the i-th energy supply plant at the end of the period, represents the fuel consumption of the i-th energy supply plant, represents the initial fuel inventory of the i-th energy supply plant, represents the initial fuel in-transit quantity of the i-th energy supply plant, Represents the fuel supply of the i-th energy supply plant.

[0035] Specifically, according to Figure 2 From the architecture, we can see that the fuel inventory increment of the energy plant in the inventory module = Ending inventory of fuel in the inventory module - End of period fuel in transit in the inventory module + Fuel consumption of the energy plant in the consumption module - Beginning inventory of fuel in the inventory module - The initial fuel quantity in transit in the inventory module - Fuel supply to the energy plant .

[0036] Specifically, the calculation rules for the fuel supply of the energy supply plant are as follows: ;in, represents the fuel supply of the ith energy plant, represents the variable fuel supply of the nth supply mode of the i-th energy supply plant, represents the fixed fuel supply of the i-th energy plant.

[0037] according to Figure 2 From the architecture, we can see that the supply module contains two parts of fuel supply, one is the fixed supply of fuel (i.e. ), and the other part is the variable supply of fuel from different supply methods of the energy supply plant (i.e. ), so the fuel supply of the energy plant = the sum of the variable fuel supply of different supply methods of the energy plant + the fixed fuel supply.

[0038] In one embodiment, the constraints of the fuel planning optimization scheduling objective function include: fuel inventory constraints and fuel supply constraints; The fuel inventory constraints are: ;in, represents the initial fuel in-transit quantity of the i-th energy supply plant, represents the fuel in transit quantity of the i-th energy supply plant at the end of the period, Represents the fuel inventory at the end of the i-th energy supply plant.

[0039] The fuel supply constraints are: ;in, represents the variable fuel supply of the nth supply mode of the i-th energy supply plant, It represents the maximum variable fuel supply of the nth supply mode of the ith energy supply plant.

[0040] S200, searching and solving the fuel plan optimization scheduling objective function to determine the optimal fuel variable supply quantity of different supply modes of the energy supply plant that meets the constraint conditions; Specifically, in an embodiment of the invention, the fuel plan optimization scheduling objective function can be directly optimized and solved to obtain the optimal variable fuel supply of different supply methods of the energy supply plant that meets the constraints. The target optimization algorithm can also be used to optimize the fuel plan optimization scheduling objective function to obtain the optimal variable fuel supply of different supply methods of the energy supply plant that meets the constraints.

[0041] Objective optimization algorithms are a class of mathematical methods used to find the best solution under given constraints in order to maximize or minimize one or more objective functions. These algorithms are widely used in fields such as engineering, economics, decision science, and machine learning.

[0042] In one embodiment, the multi-objective optimization algorithm includes, but is not limited to: Linear Programming (LP), Nonlinear Programming (NLP), Integer Programming (IP), Dynamic Programming (DP), Genetic Algorithms (GA), Particle Swarm Optimization (PSO), Simulated Annealing (SA), Neural Network Optimization. This embodiment of the present invention does not specifically limit this.

[0043] The following is a detailed introduction to the multi-objective optimization algorithm: Linear programming: Linear programming is a method in mathematical optimization that is used to solve the optimal value (maximum or minimum value) of a linear objective function under given linear inequality or equality constraints. It is widely used in resource allocation, production planning, transportation problems and other fields.

[0044] Nonlinear programming: Nonlinear programming is to solve the optimization problem when at least one of the objective function or constraints is nonlinear. It is more complicated than linear programming because nonlinear problems usually have no general solution and need to be designed according to the specific problem.

[0045] Integer Programming: Integer programming is a special case of linear programming or nonlinear programming where the variables must be integers. In practical problems, integer programming is used in situations where the variables must be integers, such as project selection, equipment purchase, etc.

[0046] Dynamic programming: Dynamic programming is a method for solving complex problems in mathematics, management science, economics, bioinformatics, etc. It solves optimization problems by breaking them down into relatively simple sub-problems, and is particularly suitable for solving multi-stage decision-making process problems.

[0047] Genetic algorithm: Genetic algorithm is a heuristic search algorithm that simulates the principles of natural selection and genetics and optimizes problem solutions through selection, crossover and mutation operations. It is suitable for solving complex problems that are difficult to solve with traditional optimization methods.

[0048] Particle Swarm Optimization: Particle swarm optimization is an optimization tool based on swarm intelligence that simulates the social behavior of bird flocks or fish schools to find the optimal solution to the problem. Each "particle" represents a candidate solution in the problem space and adjusts its position by tracking its own historical best position and the best position of the entire group.

[0049] Simulated annealing: Simulated annealing is a general probabilistic algorithm used to find the global optimal solution to a given problem. The algorithm is inspired by the annealing process of solid materials and searches the solution space globally by slowly reducing the "temperature" (control parameter) to avoid falling into the local optimal solution.

[0050] Neural Network Optimization: Neural network optimization is the use of artificial neural networks to solve optimization problems. It usually involves training a neural network to predict or classify data and minimizing the prediction error by adjusting the network's weights and biases.

[0051] In order to understand how to use the optimization algorithm to solve the objective function of coal transportation cost, the following example is used to illustrate the particle swarm optimization: Step 1, initialize the particle swarm: randomly generate a certain number of particles, each particle represents a candidate solution in the problem space (i.e., the variable supply of fuel in different supply modes of the energy plant). Each particle has its own position and speed. The position corresponds to the solution space of the objective function, and the speed determines the speed and direction of the particle's movement in the solution space.

[0052] Step 2, evaluate particle fitness: calculate the value of the objective function (i.e., the inventory growth difference rate) for each particle's position, and use this value as the particle's fitness. In optimization problems, it is usually desirable to minimize the objective function, so the lower the fitness, the better the solution.

[0053] Step 3, update the individual best and global best: each particle will remember its best position in history, and the entire particle group will share the global best position found so far.

[0054] Step 4, update particle speed and position: Update the speed and position of each particle based on the current speed, individual best position and global best position. The speed update formula usually contains inertia weight, cognitive component and social component, and the position update is based on the new speed.

[0055] Step 5, iterative optimization: Repeat steps 2 to 4 until the termination condition is met, such as reaching the maximum number of iterations, the change in the global best fitness is less than a certain threshold, or the accuracy requirement of the problem is met.

[0056] Step 6, output result: The final global optimal position is the approximate optimal solution of the objective function found by the algorithm.

[0057] S300, sending the optimal variable fuel supply quantity of different supply modes of the energy supply plant to the energy supply plant, so that the energy supply plant adjusts the fuel procurement plan according to the optimal variable fuel supply quantity of different supply modes of the energy supply plant.

[0058] The procurement plan includes: variable supply quantities of fuel plans for different supply methods of the energy plant.

[0059] For each supply method of the energy plant: Specifically, when the difference between the optimal variable fuel supply of the energy supply plant and the planned variable fuel supply corresponding to the energy supply plant is greater than a preset threshold, the planned variable fuel supply corresponding to the energy supply plant is corrected to the optimal variable fuel supply of the energy supply plant to update the fuel procurement plan.

[0060] The following are examples: Assume that a power plant mainly generates electricity by purchasing coal as fuel, and pre-sets a variable supply range of the fuel plan. At the same time, it will derive an optimal variable fuel supply based on some optimization algorithms, and the preset threshold is set to 5 tons.

[0061] Initial data The planned variable supply of fuel for the energy plant: It is planned that in the next production cycle, the planned variable supply of coal will range from 30 to 50 tons, and the currently set planned variable supply of fuel is 40 tons (that is, 40 tons is selected as the planned supply within the range allowed by the plan).

[0062] The optimal variable fuel supply of the energy supply plant: After optimization calculation, the optimal variable fuel supply is 48 tons, that is, supplying fuel according to this 48 tons, which can enable the energy supply plant to achieve better comprehensive benefits under various current conditions.

[0063] Difference comparison and processing At this time, calculate the difference between the two: the optimal fuel variable supply (48 tons) minus the planned fuel variable supply (40 tons), the difference is 48-40=8 tons.

[0064] Because 8 tons is greater than the preset threshold of 5 tons, the fuel plan variable supply corresponding to the energy supply plant must be corrected to the optimal fuel variable supply of the energy supply plant according to the rules, that is, the fuel plan variable supply is corrected from 40 tons to 48 tons, and then the fuel procurement plan is updated based on this corrected value of 48 tons. The subsequent procurement department will prepare corresponding fuel procurement matters according to the coal procurement volume of 48 tons to ensure that the overall plan arrangement meets the fuel supply of the energy supply plant.

[0065] Beneficial effects of the present invention: (1) Inventory management optimization: By constructing a function with the minimum inventory growth difference rate as the goal, it helps to achieve a stable growth of fuel inventory in energy supply plants. It avoids the increase in management costs and potential supply risks caused by large inventory fluctuations, makes inventory management more scientific and orderly, and improves the utilization efficiency of inventory funds.

[0066] (2) Reasonable allocation of resources: Accurately calculate the variable supply and optimal value of fuel under different supply methods, and reasonably allocate fuel resources according to actual needs. Ensure that each energy supply plant obtains the appropriate amount of fuel under different supply methods, avoid excessive concentration or shortage of resources, and improve the stability and reliability of the overall energy supply.

[0067] (3) Accurate adjustment of procurement plan: Based on the comparison and correction mechanism between the optimal variable fuel supply and the planned supply, the fuel procurement plan is made more in line with the actual operational needs. This reduces unnecessary procurement costs, avoids fuel backlogs or shortages caused by unreasonable plans, and improves the accuracy and flexibility of procurement plans.

[0068] (4) Improve operational efficiency: Comprehensively consider fuel inventory constraints and supply constraints to ensure the feasibility of the optimization plan in actual operation. This helps the energy supply plant to meet production needs while complying with inventory and supply constraints, optimize production and operation processes, and ultimately improve the overall operational efficiency and economic benefits of the energy supply plant.

[0069] Embodiment 2 Based on the same inventive concept, Figure 3 As shown, the embodiment of the present invention further provides a fuel plan optimization device 200, comprising: The function construction module 210 is used to construct a fuel plan optimization scheduling objective function based on the variable fuel supply of different supply modes of the energy supply plant and to minimize the inventory growth difference rate, and to determine the constraint conditions of the fuel plan optimization scheduling objective function; The optimization solution module 220 is used to optimize and solve the fuel plan optimization scheduling objective function to determine the optimal fuel variable supply quantity of different supply modes of the energy supply plant that meets the constraint conditions; The planning module 230 is used to send the optimal variable fuel supply quantity of different supply modes of the energy supply plant to the energy supply plant, so that the energy supply plant adjusts the fuel procurement plan according to the optimal variable fuel supply quantity of different supply modes of the energy supply plant.

[0070] It should be understood that the device corresponds to the above-mentioned fuel planning optimization method embodiment and can execute the various steps involved in the above-mentioned method embodiment. The specific functions of the device can be found in the above description. To avoid repetition, the detailed description is appropriately omitted here. The device includes at least one software function module that can be stored in a memory in the form of software or firmware or fixed in the operating system (OS) of the device.

[0071] Embodiment 3 Based on the same inventive concept, an embodiment of the present invention further provides an electronic device, including: a processor and a memory, the memory storing machine-readable instructions executable by the processor, and the machine-readable instructions executing the above-mentioned fuel planning optimization method when executed by the processor.

[0072] In a typical configuration, an electronic device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0073] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0074] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0075] Embodiment 4 Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on a computer, the computer executes the above-mentioned fuel planning optimization method.

[0076] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0077] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0078] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0079] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0080] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe various possible combinations.

[0081] In addition, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist separately, or two or more modules can be integrated to form an independent part.

[0082] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity 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, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0083] The above are only embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included within the scope of the claims of the present application.

Claims

1. A fuel planning optimization method, characterized in that: include: Based on the variable fuel supply of different supply modes of the energy supply plant, the fuel planning optimization scheduling objective function is constructed with the goal of minimizing the inventory growth difference rate, and the constraints of the fuel planning optimization scheduling objective function are determined; The optimal fuel scheduling objective function is solved to determine the optimal variable fuel supply of different supply modes of the energy supply plant that meets the constraints; The optimal variable fuel supply quantities of different supply modes of the energy supply plant are sent to the energy supply plant, so that the energy supply plant adjusts the fuel procurement plan according to the optimal variable fuel supply quantities of different supply modes of the energy supply plant.

2. The fuel planning optimization method according to claim 1, characterized in that: The objective function of fuel planning optimization scheduling is: ;in, represents the inventory growth variance rate, represents the fuel inventory increment of the i-th energy supply plant, represents the initial fuel inventory of the i-th energy supply plant, Indicates the number of energy supply plants.

3. The fuel planning optimization method according to claim 2, characterized in that: The calculation rules for the fuel inventory increase of the energy supply plant are as follows: ;in, represents the fuel inventory increment of the i-th energy supply plant, represents the fuel inventory at the end of the period of the i-th energy supply plant, represents the fuel in transit quantity of the i-th energy supply plant at the end of the period, represents the fuel consumption of the i-th energy supply plant, represents the initial fuel inventory of the i-th energy supply plant, represents the initial fuel in-transit quantity of the i-th energy supply plant, Represents the fuel supply of the i-th energy supply plant.

4. The fuel planning optimization method according to claim 3, characterized in that: The calculation rules for the fuel supply of the energy plant are as follows: ;in, represents the fuel supply of the ith energy plant, represents the variable fuel supply of the nth supply mode of the i-th energy supply plant, represents the fixed fuel supply of the i-th energy plant.

5. The fuel planning optimization method according to claim 2, characterized in that: The constraints of the objective function of fuel planning optimization scheduling include: fuel inventory constraints; The fuel inventory constraints are: ;in, represents the initial fuel in-transit quantity of the i-th energy supply plant, represents the fuel in transit quantity of the i-th energy supply plant at the end of the period, Represents the fuel inventory at the end of the i-th energy supply plant.

6. The fuel planning optimization method according to claim 2, characterized in that: The constraints of the objective function of fuel planning optimization scheduling include: fuel supply constraints; The fuel supply constraints are: ;in, represents the variable fuel supply of the nth supply mode of the i-th energy supply plant, It represents the maximum variable fuel supply of the nth supply mode of the ith energy supply plant.

7. The fuel planning optimization method according to claim 1, characterized in that: The procurement plan includes: planned variable supply quantities of fuel for different supply methods of the energy plant; Adjust the fuel procurement plan based on the optimal variable fuel supply for different supply methods of the energy plant, including: For each supply method of the energy plant: When the difference between the optimal variable fuel supply of the energy supply plant and the planned variable fuel supply corresponding to the energy supply plant is greater than a preset threshold, the planned variable fuel supply corresponding to the energy supply plant is corrected to the optimal variable fuel supply of the energy supply plant to update the fuel procurement plan.

8. A fuel planning optimization device, characterized in that: include: A function building module is used to build a fuel planning optimization scheduling objective function based on the variable fuel supply of different supply modes of the energy supply plant, with the goal of minimizing the inventory growth difference rate, and determine the constraints of the fuel planning optimization scheduling objective function; The optimization solution module is used to optimize and solve the fuel plan optimization scheduling objective function to determine the optimal fuel variable supply quantity of different supply modes of the energy supply plant that meets the constraint conditions; The planning module is used to send the optimal variable fuel supply quantity of different supply modes of the energy supply plant to the energy supply plant, so that the energy supply plant adjusts the fuel procurement plan according to the optimal variable fuel supply quantity of different supply modes of the energy supply plant.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and when the machine-readable instructions are executed by the processor, the fuel planning optimization method according to any one of claims 1 to 7 is executed.

10. A computer-readable storage medium storing computer instructions, characterized in that: When the computer instructions are executed on a computer, the computer is enabled to execute the fuel planning optimization method according to any one of claims 1 to 7.

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