Electrolytic aluminum enterprise power purchase strategy optimization method and device based on intelligent contract, medium and equipment

Abstract

The invention discloses an electrolytic aluminum enterprise power purchase strategy optimization method and device based on an intelligent contract, a medium and equipment, and the method comprises the steps: enabling an electrolytic aluminum enterprise to generate a plurality of purchase strategies based on the minimization of the power purchase cost as a target, and enabling a power market operator to generate a plurality of purchase strategies based on the maximization of the operation profit as a target; and the strategies are comprehensively sorted through an intelligent contract, so that an optimal purchase strategy combination is determined. The electrolytic aluminum enterprise adjusts electric power purchase according to the target power of the combination, and it is ensured that electric power use in the production process is more efficient and economical.

Description

Technical Field

[0001] The present invention relates to the technical field of market decision optimization, and in particular to a method, device, medium and equipment for optimizing the electricity procurement strategy of an electrolytic aluminum enterprise based on a smart contract. Background Art

[0002] At present, most electrolytic aluminum enterprises still adopt the traditional electricity purchasing model, which usually purchases electricity based on fixed prices or historical average prices. Since this method cannot be flexibly adjusted according to the fluctuations of market electricity prices, enterprises fail to make full use of low-priced electricity during periods of low electricity prices, thereby increasing unnecessary costs. Summary of the invention

[0003] Based on this, it is necessary to provide methods, devices, media and equipment for optimizing electricity procurement strategies for electrolytic aluminum enterprises based on smart contracts to solve the problem that enterprises cannot flexibly adjust according to fluctuations in market electricity prices.

[0004] A method for optimizing electric power procurement strategy of an electrolytic aluminum enterprise based on a smart contract is applied to an electric power procurement strategy optimization system. The electric power procurement strategy optimization system includes an electrolytic aluminum enterprise, an electric power market operator and a smart contract. The electrolytic aluminum enterprise has a correspondingly constructed production constraint model. The method includes:

[0005] The electrolytic aluminum enterprise obtains the first electricity purchase data of the production process in real time, and generates a plurality of first electricity purchase strategies according to the first electricity purchase data using the electricity purchase cost minimization model under the constraint of the production constraint model, determines the first preference order of each first electricity purchase strategy according to the electricity purchase cost, and submits all the first preference orders to the smart contract;

[0006] The power market operator obtains the second power purchasing data in real time, generates a plurality of second power purchasing strategies using an operation profit maximization model according to the second power purchasing data, determines a second preference order for each second power purchasing strategy according to the operation profit, and submits all the second preference orders to the smart contract;

[0007] The smart contract determines the comprehensive preference order of different procurement strategy combinations according to the first preference order and the second preference order, takes the power setting value corresponding to the procurement strategy combination with the highest comprehensive preference order as the target power, and sends it to the electrolytic aluminum enterprise, so that the electrolytic aluminum enterprise purchases and produces electricity based on the target power; wherein each procurement strategy combination includes a first power procurement strategy and a second power procurement strategy.

[0008] In one embodiment, the method further comprises:

[0009] Construct the production power constraint model, which is expressed as:

[0010]

[0011] In the above formula, is the power of electrolytic cell series l of aluminum electrolytic enterprise i at time t; is the minimum value of the electrolytic cell power; is the maximum value of the electrolytic cell power; is the voltage applied to electrolytic cell series l of aluminum electrolytic enterprise i at time t; is the current intensity of electrolytic cell series l of aluminum electrolytic enterprise i at time t;

[0012] Construct the temperature constraint model, expressed as:

[0013]

[0014] In the above formula, T e,l,t is the temperature of electrolytic cell series l at time t; T e,max is the maximum allowable temperature of the electrolytic cell; T e,min is the minimum allowable temperature of the electrolytic cell; t1, t2, t3t4, k are constant coefficients; T is the total time scale;

[0015] Construct a daily production constraint model, expressed as:

[0016]

[0017] In the above formula, is the daily energy consumption of electrolytic aluminum enterprise i; is the minimum value of daily energy consumption; is the maximum value of daily energy consumption; N ea is the total number of electrolytic cell series;

[0018] The production constraint model is a combination of the production power constraint model, the temperature constraint model and the daily output constraint model.

[0019] In one embodiment, generating a plurality of first electricity purchasing strategies using an electricity purchasing cost minimization model according to the first electricity purchasing data includes:

[0020] Substitute the first electricity purchase data into the electricity purchase cost minimization model; wherein the electricity purchase cost minimization model is expressed as:

[0021]

[0022] In the above formula, is the electricity purchase cost of electrolytic aluminum enterprise i; α buy is the unit electricity purchase price; α buy , T is the first electricity purchase data;

[0023] The electricity purchase cost minimization model and production constraints are converted into a linear programming model input format, and solved by an optimization solver to obtain a plurality of first electricity purchase strategies.

[0024] In one embodiment, determining the first preference order of each first electricity purchasing strategy according to the electricity purchasing cost includes:

[0025] The preference order of the first electricity purchasing strategy with a lower daily total electricity purchasing cost is arranged to be higher, so as to obtain the first preference order.

[0026] In one embodiment, generating a plurality of second electricity purchasing strategies using an operation profit maximization model according to the second electricity purchasing data includes:

[0027] Substitute the second electricity purchase data into the operating profit maximization model; wherein the operating profit maximization model is expressed as:

[0028]

[0029] In the above formula, is the revenue obtained by the electricity market operator from the electrolytic aluminum enterprise i at time t; The power corresponding to the electricity sold to the grid by the electricity market operator; The power corresponding to the electricity purchased by the electricity market operator from the power grid; π sell is the electricity selling price at time t; π buy is the wholesale electricity price at time t; T1 is the electricity selling period; T2 is the electricity purchasing period; π sell , π buy , T1, T2 are the second electricity purchase data;

[0030] The operating profit maximization model is converted into a linear programming model input format and solved by an optimization solver to obtain a plurality of second electricity purchasing strategies.

[0031] In one embodiment, determining the second preference order of each second power procurement strategy according to the operating profit includes:

[0032] The preference order of the second power purchasing strategy with a higher daily total operating profit is arranged to be higher, so as to obtain the second preference order.

[0033] In one embodiment, the power setting value corresponding to the purchase strategy combination with the highest comprehensive preference order is used as the target power, which is expressed as:

[0034]

[0035] In the above formula, Indicates the target power; B indicates the adjustable power range; Represents the first preference order of the electrolytic aluminum enterprise i for the power setting value b at time t It represents the second preference order of the electricity market operator for the power setting value b of the electrolytic aluminum enterprise i at time t.

[0036] An electric power procurement strategy optimization device for an electrolytic aluminum enterprise based on a smart contract is applied to an electric power procurement strategy optimization system. The electric power procurement strategy optimization system includes an electrolytic aluminum enterprise, an electric power market operator and a smart contract. The electrolytic aluminum enterprise has a correspondingly constructed production constraint model. The electric power procurement strategy optimization device for an electrolytic aluminum enterprise based on a smart contract includes:

[0037] A first preference order determination module is used for the electrolytic aluminum enterprise to obtain first electricity purchase data of the production process in real time, and under the constraints of the production constraint model, generate multiple first electricity purchase strategies according to the first electricity purchase data using an electricity purchase cost minimization model, determine the first preference order of each first electricity purchase strategy according to the electricity purchase cost, and submit all the first preference orders to the smart contract;

[0038] A second preference order determination module is used for the power market operator to obtain second power purchase data in real time, generate multiple second power purchase strategies based on the second power purchase data using an operation profit maximization model, determine a second preference order for each second power purchase strategy based on the operation profit, and submit all second preference orders to the smart contract;

[0039] A procurement optimization module is used for the smart contract to determine the comprehensive preference order of different procurement strategy combinations according to the first preference order and the second preference order, and to use the power setting value corresponding to the procurement strategy combination with the highest comprehensive preference order as the target power, and send it to the electrolytic aluminum enterprise, so that the electrolytic aluminum enterprise purchases and produces electricity based on the target power; wherein each procurement strategy combination includes a first power procurement strategy and a second power procurement strategy.

[0040] A computer-readable storage medium stores a computer program. When the computer program is executed by a processor, the processor executes the steps of the above-mentioned method for optimizing the electricity procurement strategy of an electrolytic aluminum enterprise based on smart contracts.

[0041] A terminal device includes a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the above-mentioned method for optimizing the electricity procurement strategy of an electrolytic aluminum enterprise based on smart contracts.

[0042] The present invention provides a method, device, medium and equipment for optimizing the power purchasing strategy of an electrolytic aluminum enterprise based on a smart contract, wherein the electrolytic aluminum enterprise generates multiple purchasing strategies based on the goal of minimizing the power purchase cost, and the power market operator generates multiple purchasing strategies based on the goal of maximizing the operating profit, and these strategies are comprehensively ranked through smart contracts to determine the optimal purchasing strategy combination. The electrolytic aluminum enterprise adjusts the power purchase according to the target power of the combination to ensure that the power use in the production process is more efficient and economical. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0044] in:

[0045] Figure 1 A flowchart of the optimization method of electricity procurement strategy for electrolytic aluminum enterprises based on smart contracts;

[0046] Figure 2 This is a schematic diagram of the structure of the power procurement strategy optimization device for electrolytic aluminum enterprises based on smart contracts;

[0047] Figure 3 This is a structural block diagram of the terminal equipment. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. 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 limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0050] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0051] like Figure 1 As shown, Figure 1 The flowchart of the method for optimizing the power purchasing strategy of an electrolytic aluminum enterprise based on a smart contract in one embodiment is applied to a power purchasing strategy optimization system, which includes an electrolytic aluminum enterprise, a power market operator and a smart contract; wherein the smart contract is an automated program embedded with blockchain technology, which automatically executes the contract terms based on predefined rules. The electrolytic aluminum enterprise is the main electricity user in the system, and its goal is to minimize the electricity cost through the power purchasing plan formulated by the smart contract. The power market operator is the main body that provides power resources and participates in power transactions, and its goal is to maximize profits through the price difference of electricity.

[0052] Aluminum electrolytic enterprises have corresponding production constraint models, including production power constraint model, temperature constraint model and daily output constraint model.

[0053] The production power constraint model is expressed as:

[0054]

[0055] In the above formula, is the power of electrolytic cell series l of aluminum electrolytic enterprise i at time t; is the minimum value of the electrolytic cell power; is the maximum value of the electrolytic cell power; is the voltage applied to electrolytic cell series l of aluminum electrolytic enterprise i at time t; is the current intensity of electrolytic cell series l of aluminum electrolytic enterprise i at time t;

[0056] The temperature constraint model is expressed as:

[0057]

[0058] In the above formula, T e,l,t is the temperature of electrolytic cell series l at time t; T e,max is the maximum allowable temperature of the electrolytic cell; T e,min is the minimum allowable temperature of the electrolytic cell; t1, t2, t3t4, k are constant coefficients; T is the total time scale;

[0059] The daily production constraint model is expressed as:

[0060]

[0061] In the above formula, is the daily energy consumption of electrolytic aluminum enterprise i; is the minimum value of daily energy consumption; is the maximum value of daily energy consumption; N ea is the total number of electrolytic cell series;

[0062] By constructing the above-mentioned over-constrained model, it is possible to maximize the economic benefits in the process of electricity procurement and use while meeting production targets, taking into account the needs of enterprises and electricity market operators.

[0063] The steps provided by the method for optimizing the electric power procurement strategy of an electrolytic aluminum enterprise based on smart contracts in this embodiment include:

[0064] S101, the electrolytic aluminum enterprise obtains the first electricity purchasing data of the production process in real time, and under the constraints of the production constraint model, generates multiple first electricity purchasing strategies based on the first electricity purchasing data using the electricity purchasing cost minimization model, determines the first preference order of each first electricity purchasing strategy based on the electricity purchasing cost, and submits all the first preference orders to the smart contract.

[0065] Among them, the first electricity purchase data refers to the real-time data related to electricity purchase obtained by the electrolytic aluminum enterprise at the current moment. It includes but is not limited to real-time electricity price fluctuations (such as time-of-use electricity prices, market transaction prices), the current electricity demand of the enterprise (production load curve, production plan), the power generation capacity of the enterprise's own power plant (such as wind power, cogeneration), historical electricity price trends and forecast data, etc. Each electrolytic aluminum enterprise will be equipped with an enterprise energy management system, through which key data such as real-time electricity price fluctuations, enterprise electricity demand and load curves can be tracked, so that subsequent electricity procurement decisions can quickly respond to market fluctuations and enterprise needs.

[0066] The production constraint model is a collection of constraints such as production power, temperature, daily output, and power balance mentioned above. These constraints ensure that the generated power procurement strategy meets the production requirements and technical feasibility of the enterprise, and avoids negative impacts on production due to insufficient or improper power allocation. For example, if a strategy causes the power to be lower than Pmin, the strategy will be automatically excluded.

[0067] The electricity purchase cost minimization model calculates the electricity purchasing strategy of electrolytic aluminum enterprises under real-time data and constraints through an optimization algorithm, with the goal of minimizing the total electricity purchase cost.

[0068] Optionally, generating a plurality of first electricity purchasing strategies using an electricity purchasing cost minimization model according to the first electricity purchasing data specifically includes the following steps:

[0069] A1. Substitute the first electricity purchase data into the electricity purchase cost minimization model.

[0070] Among them, the electricity purchase cost minimization model is expressed as:

[0071]

[0072] In the above formula, is the electricity purchase cost of electrolytic aluminum enterprise i; α buy is the unit electricity purchase price, which changes over time; α buy , T is the first electricity purchase data, which is also a variable that needs to be optimized;

[0073] A2. Convert the electricity purchase cost minimization model and production constraints into a linear programming model input format, and solve them through an optimization solver to obtain multiple first electricity purchase strategies.

[0074] The optimization solver here can be Gurobi, CPLEX or the open source tool LPSolve. These optimization solvers can quickly solve linear programming problems and output multiple optimal solutions that meet the constraints (i.e., multiple first power procurement strategies).

[0075] Furthermore, the preference order of the first electricity purchasing strategy with a lower daily total electricity purchasing cost is made higher, so as to obtain the first preference order.

[0076] That is to say, multiple first power purchasing strategies are ranked according to the total daily power purchasing cost (the value of the objective function f1). The lower the total power purchasing cost, the better the strategy and the higher the preference order. Through this ranking, the most economical and efficient purchasing strategy can be quickly identified.

[0077] S102, the power market operator obtains the second power purchasing data in real time, generates multiple second power purchasing strategies based on the second power purchasing data using the operating profit maximization model, determines the second preference order of each second power purchasing strategy based on the operating profit, and submits all the second preference orders to the smart contract.

[0078] Among them, the second power purchase data refers to the power transaction-related data obtained by the power market operator in real time, including: Wholesale power price: the unit price of wholesale power in the current market. Power demand curve: the market demand for power in each time period. Enterprise power load: the expected load or power demand information submitted by each electrolytic aluminum enterprise. The second power purchase data is the basic information for power market operators to optimize their power procurement strategies and evaluate profits.

[0079] The operation profit maximization model aims to maximize the economic benefits of power market operators by optimizing power purchase and sales strategies. The operator generates a feasible power purchase plan based on the operation profit maximization model and the second power purchase data, including the specific arrangements of power purchase time, power purchase amount, power sales time and power sales amount.

[0080] Optionally, generating a plurality of second electricity purchasing strategies using an operation profit maximization model according to the second electricity purchasing data specifically includes the following steps:

[0081] B1. Substitute the second electricity purchase data into the operating profit maximization model.

[0082] Among them, the operating profit maximization model is expressed as:

[0083]

[0084] In the above formula, is the revenue obtained by the electricity market operator from the electrolytic aluminum enterprise i at time t; The power corresponding to the electricity sold to the grid by the electricity market operator; The power corresponding to the electricity purchased by the electricity market operator from the power grid; π sell is the electricity selling price at time t; π buy is the wholesale electricity price at time t; T1 is the electricity selling period; T2 is the electricity purchasing period; π sell , π buy , T1, T2 are the second electricity purchase data;

[0085] B2. Convert the operating profit maximization model into a linear programming model input format, and solve it through an optimization solver to obtain multiple second power procurement strategies.

[0086] The optimization solver here can be Gurobi, CPLEX or the open source tool LPSolve, through which the linear programming problem can be quickly solved and multiple optimal solutions that meet the constraints can be output (i.e., multiple second power procurement strategies).

[0087] Furthermore, the preference order of the second power purchasing strategy with a higher daily total operating profit is made higher, so as to obtain a second preference order.

[0088] That is to say, multiple second power procurement strategies are ranked according to the total daily operating profit (the value of the objective function f2). The higher the total operating profit, the better the strategy and the higher the preference order. Through this ranking, the most economical and efficient procurement strategy can be quickly identified.

[0089] S103, the smart contract determines the comprehensive preference order of different procurement strategy combinations according to the first preference order and the second preference order, takes the power setting value corresponding to the procurement strategy combination with the highest comprehensive preference order as the target power, and sends it to the electrolytic aluminum enterprise, so that the electrolytic aluminum enterprise purchases and produces electricity based on the target power.

[0090] Each purchase strategy combination includes a first power purchase strategy and a second power purchase strategy.

[0091] Optionally, the power setting value corresponding to the purchase strategy combination with the highest comprehensive preference order is used as the target power, which is expressed as:

[0092]

[0093] In the above formula, Indicates the target power; B indicates the adjustable power range; Represents the first preference order of the electrolytic aluminum enterprise i for the power setting value b at time t It represents the second preference order of the electricity market operator for the power setting value b of the electrolytic aluminum enterprise i at time t.

[0094] The smart contract selects the purchase strategy combination with the highest comprehensive preference order, extracts the power consumption value corresponding to the combination, and sends it to the energy management system of the electrolytic aluminum enterprise to guide the enterprise's production.

[0095] This method not only improves the production efficiency of electrolytic aluminum enterprises, but also increases the profit margins of power market operators, while promoting the intelligent and efficient development of overall power trading.

[0096] The above-mentioned smart contract-based electrolytic aluminum enterprise power procurement strategy optimization method generates multiple procurement strategies based on the goal of minimizing the power purchase cost of the electrolytic aluminum enterprise and the goal of maximizing the operating profit of the power market operator, and comprehensively sorts these strategies through smart contracts to determine the optimal procurement strategy combination. The electrolytic aluminum enterprise adjusts the power procurement according to the target power of the combination to ensure that the power use in the production process is more efficient and economical.

[0097] In one embodiment, Figure 2 As shown in the figure, a device for optimizing the electricity purchasing strategy of an electrolytic aluminum enterprise based on a smart contract is proposed, which includes:

[0098] The first preference order determination module 201 is used for the electrolytic aluminum enterprise to obtain the first power purchase data of the production process in real time, and under the constraints of the production constraint model, generate multiple first power purchase strategies according to the first power purchase data using the power purchase cost minimization model, determine the first preference order of each first power purchase strategy according to the power purchase cost, and submit all the first preference orders to the smart contract;

[0099] A second preference order determination module 202 is used for the power market operator to obtain the second power purchase data in real time, generate multiple second power purchase strategies based on the second power purchase data using the operation profit maximization model, determine the second preference order of each second power purchase strategy based on the operation profit, and submit all the second preference orders to the smart contract;

[0100] The procurement optimization module 203 is used for the smart contract to determine the comprehensive preference order of different procurement strategy combinations according to the first preference order and the second preference order, and use the power setting value corresponding to the procurement strategy combination with the highest comprehensive preference order as the target power, and send it to the electrolytic aluminum enterprise, so that the electrolytic aluminum enterprise purchases and produces electricity based on the target power; wherein each procurement strategy combination includes a first power procurement strategy and a second power procurement strategy.

[0101] Figure 3 FIG. 2 shows an internal structure diagram of a terminal device in an embodiment. Figure 3 As shown, the terminal device includes a processor, a memory and a network interface connected via a system bus. Among them, the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the terminal device stores an operating system and may also store a computer program. When the computer program is executed by the processor, the processor can implement an optimization method for the electricity procurement strategy of an electrolytic aluminum enterprise based on a smart contract. The internal memory may also store a computer program. When the computer program is executed by the processor, the processor can implement an optimization method for the electricity procurement strategy of an electrolytic aluminum enterprise based on a smart contract. Those skilled in the art can understand that Figure 3 The structure shown in the figure is only a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the terminal device to which the scheme of the present application is applied. The specific terminal device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0102] A computer-readable storage medium stores a computer program, which implements the following steps when executed by a processor: an electrolytic aluminum enterprise obtains first power purchase data of a production process in real time, and generates multiple first power purchase strategies based on the first power purchase data using a power purchase cost minimization model under the constraints of a production constraint model, determines a first preference order for each first power purchase strategy based on the power purchase cost, and submits all first preference orders to a smart contract; a power market operator obtains second power purchase data in real time, generates multiple second power purchase strategies based on the second power purchase data using an operating profit maximization model, determines a second preference order for each second power purchase strategy based on the operating profit, and submits all second preference orders to a smart contract; the smart contract determines a comprehensive preference order of different purchase strategy combinations based on the first preference order and the second preference order, takes the power consumption set value corresponding to the purchase strategy combination with the highest comprehensive preference order as the target power, and sends it to the electrolytic aluminum enterprise, so that the electrolytic aluminum enterprise purchases and produces power based on the target power; wherein each purchase strategy combination includes a first power purchase strategy and a second power purchase strategy.

[0103] A terminal device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the computer program: an electrolytic aluminum enterprise obtains first power purchase data of a production process in real time, and generates a plurality of first power purchase strategies using a power purchase cost minimization model according to the first power purchase data under the constraint of a production constraint model, determines a first preference order of each first power purchase strategy according to the power purchase cost, and submits all first preference orders to a smart contract; an electric power market operator obtains second power purchase data in real time, generates a plurality of second power purchase strategies using an operating profit maximization model according to the second power purchase data, determines a second preference order of each second power purchase strategy according to the operating profit, and submits all second preference orders to a smart contract; the smart contract determines a comprehensive preference order of different purchase strategy combinations according to the first preference order and the second preference order, takes the power consumption set value corresponding to the purchase strategy combination with the highest comprehensive preference order as the target power, and sends it to the electrolytic aluminum enterprise, so that the electrolytic aluminum enterprise purchases and produces power based on the target power; wherein each purchase strategy combination comprises a first power purchase strategy and a second power purchase strategy.

[0104] It should be noted that the above-mentioned method, device, equipment and computer-readable storage medium for optimizing the electricity procurement strategy of an electrolytic aluminum enterprise based on smart contracts belong to a general inventive concept, and the contents in the embodiments of the method, device, equipment and computer-readable storage medium for optimizing the electricity procurement strategy of an electrolytic aluminum enterprise based on smart contracts can be applied to each other.

[0105] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0106] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0107] The above embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for optimizing electricity procurement strategy for electrolytic aluminum enterprises based on smart contracts, characterized in that: Applied to a power procurement strategy optimization system, the power procurement strategy optimization system includes an electrolytic aluminum enterprise, a power market operator and a smart contract, the electrolytic aluminum enterprise has a correspondingly constructed production constraint model, and the method includes: The electrolytic aluminum enterprise obtains the first electricity purchase data of the production process in real time, and generates a plurality of first electricity purchase strategies according to the first electricity purchase data using the electricity purchase cost minimization model under the constraint of the production constraint model, determines the first preference order of each first electricity purchase strategy according to the electricity purchase cost, and submits all the first preference orders to the smart contract; The power market operator obtains the second power purchasing data in real time, generates a plurality of second power purchasing strategies using an operation profit maximization model according to the second power purchasing data, determines a second preference order for each second power purchasing strategy according to the operation profit, and submits all the second preference orders to the smart contract; The smart contract determines the comprehensive preference order of different procurement strategy combinations according to the first preference order and the second preference order, takes the power setting value corresponding to the procurement strategy combination with the highest comprehensive preference order as the target power, and sends it to the electrolytic aluminum enterprise, so that the electrolytic aluminum enterprise purchases and produces electricity based on the target power; wherein each procurement strategy combination includes a first power procurement strategy and a second power procurement strategy.

2. The method according to claim 1, characterized in that: The method further comprises: Construct the production power constraint model, which is expressed as: In the above formula, is the power of electrolytic cell series l of aluminum electrolytic enterprise i at time t; is the minimum value of the electrolytic cell power; is the maximum value of the electrolytic cell power; is the voltage applied to electrolytic cell series l of aluminum electrolytic enterprise i at time t; is the current intensity of electrolytic cell series l of aluminum electrolytic enterprise i at time t; Construct the temperature constraint model, expressed as: In the above formula, T e,l,t is the temperature of electrolytic cell series l at time t; T e,max is the maximum allowable temperature of the electrolytic cell; T e,min is the minimum allowable temperature of the electrolytic cell; t1, t2, t3t4, k are constant coefficients; T is the total time scale; Construct a daily production constraint model, expressed as: In the above formula, is the daily energy consumption of electrolytic aluminum enterprise i; is the minimum value of daily energy consumption; is the maximum value of daily energy consumption; N ea is the total number of electrolytic cell series; The production constraint model is a combination of the production power constraint model, the temperature constraint model and the daily output constraint model.

3. The method according to claim 2, characterized in that The step of generating a plurality of first electricity purchasing strategies using an electricity purchasing cost minimization model according to the first electricity purchasing data comprises: Substitute the first electricity purchase data into the electricity purchase cost minimization model; wherein the electricity purchase cost minimization model is expressed as: In the above formula, is the electricity purchase cost of electrolytic aluminum enterprise i; α buy is the unit electricity purchase price; α buy , T is the first electricity purchase data; The electricity purchase cost minimization model and production constraints are converted into a linear programming model input format, and solved by an optimization solver to obtain a plurality of first electricity purchase strategies.

4. The method according to claim 1, characterized in that: The step of determining a first preference order for each first electricity purchasing strategy according to the electricity purchasing cost comprises: The preference order of the first electricity purchasing strategy with a lower daily total electricity purchasing cost is arranged to be higher, so as to obtain the first preference order.

5. The method according to claim 1, characterized in that The step of generating a plurality of second electricity purchasing strategies using an operation profit maximization model according to the second electricity purchasing data comprises: Substitute the second electricity purchase data into the operating profit maximization model; wherein the operating profit maximization model is expressed as: In the above formula, is the revenue obtained by the electricity market operator from the electrolytic aluminum enterprise i at time t; The power corresponding to the electricity sold to the grid by the electricity market operator; The power corresponding to the electricity purchased by the electricity market operator from the grid; π sell is the electricity selling price at time t; π buy is the wholesale electricity price at time t; T1 is the electricity selling period; T2 is the electricity purchasing period; π sell , π buy , T1, T2 are the second electricity purchase data; The operating profit maximization model is converted into a linear programming model input format and solved by an optimization solver to obtain a plurality of second electricity purchasing strategies.

6. The method according to claim 1, characterized in that The step of determining a second preference order for each second power purchasing strategy according to the operating profit comprises: The preference order of the second power purchasing strategy with a higher daily total operating profit is arranged to be higher, so as to obtain the second preference order.

7. The method according to claim 1, characterized in that The power setting value corresponding to the purchase strategy combination with the highest comprehensive preference order is taken as the target power, which is expressed as: In the above formula, Indicates the target power; B indicates the adjustable power range; Represents the first preference order of the electrolytic aluminum enterprise i for the power setting value b at time t It represents the second preference order of the electricity market operator for the power setting value b of the electrolytic aluminum enterprise i at time t.

8. A device for optimizing electricity purchasing strategy for electrolytic aluminum enterprises based on smart contracts, characterized in that: Applied to the power procurement strategy optimization system, the power procurement strategy optimization system includes an electrolytic aluminum enterprise, a power market operator and a smart contract, the electrolytic aluminum enterprise has a correspondingly constructed production constraint model, and the electrolytic aluminum enterprise power procurement strategy optimization device based on the smart contract includes: A first preference order determination module is used for the electrolytic aluminum enterprise to obtain first electricity purchase data of the production process in real time, and under the constraints of the production constraint model, generate multiple first electricity purchase strategies according to the first electricity purchase data using an electricity purchase cost minimization model, determine the first preference order of each first electricity purchase strategy according to the electricity purchase cost, and submit all the first preference orders to the smart contract; A second preference order determination module is used for the power market operator to obtain second power purchase data in real time, generate multiple second power purchase strategies based on the second power purchase data using an operation profit maximization model, determine a second preference order for each second power purchase strategy based on the operation profit, and submit all second preference orders to the smart contract; A procurement optimization module is used for the smart contract to determine the comprehensive preference order of different procurement strategy combinations according to the first preference order and the second preference order, and to use the power setting value corresponding to the procurement strategy combination with the highest comprehensive preference order as the target power, and send it to the electrolytic aluminum enterprise, so that the electrolytic aluminum enterprise purchases and produces electricity based on the target power; wherein each procurement strategy combination includes a first power procurement strategy and a second power procurement strategy.

9. A computer-readable storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

10. A terminal device, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and when the computer program is executed by the processor, the processor executes the steps of the method according to any one of claims 1 to 7.

Images