Load aggregator and high-energy-consumption industrial user interactive operation method, system and device based on master-slave game, and medium

Abstract

The invention discloses a load aggregator and high-energy-consumption industrial user interactive operation method, system and device based on a master-slave game, and a medium, and belongs to the technical field of power system optimization and carbon emission control, and the method comprises the steps: collecting the data needed by operation, such as load, electricity price and carbon price; constructing a carbon emission model and an electric energy consumption model of the high-energy-consumption industrial user; respectively establishing a revenue function of a load aggregator and an operation cost function of a high-energy-consumption industrial user; the functions are constructed into a double-layer game model, the upper layer sets electricity price and carbon price strategies by a load aggregator, and the lower layer responds to adjust electricity consumption and yield by an industrial user; a game optimization method is adopted, strategy feedback and updating are achieved in multiple rounds of iteration, and finally convergence to a game equilibrium solution is achieved. According to the invention, multi-agent collaborative optimization under power-carbon collaborative scheduling can be realized, and the resource allocation efficiency and the carbon transaction response capability are improved.

Description

Technical Field

[0001] This invention relates to the field of power system optimization and carbon emission control technology, specifically to a method, system, equipment, and medium for interactive operation between load aggregators and high-energy-consuming industrial users based on master-slave game theory. Background Technology

[0002] In terms of market optimization decisions for high-energy-consuming industrial users, the current approach often employs a phased, independent optimization strategy for the electricity market and carbon market. Specifically, in the electricity cost optimization phase, high-energy-consuming industrial users formulate electricity-level optimization operation strategies based on time-of-use pricing or real-time pricing signals, using linear programming or mixed-integer linear programming models with the goal of minimizing electricity costs. In the carbon emission cost optimization phase, based on the electricity cost optimization results, high-energy-consuming industrial users use carbon emissions as a fixed parameter, calculate the carbon quota gap using carbon trading market rules, and incorporate carbon trading costs into the total cost.

[0003] In terms of carbon emission modeling for high-energy-consuming industrial users, existing carbon emission models are mostly designed for specific high-energy-consuming industries, relying on physical mechanism modeling or empirical formulas, making it difficult to guarantee generalization and accuracy.

[0004] In terms of optimizing the decision-making framework, existing studies mostly regard market operators and high-energy-consuming industrial users as a unified decision-making entity, aiming to minimize the total system cost, and solving the problem through centralized optimization algorithms (such as the Lagrange relaxation method).

[0005] Existing optimization technologies for high-energy-consuming industrial users in a market environment have the following shortcomings: First, existing phased optimization technologies treat electricity prices and carbon prices as independent variables, without considering their coordinated optimization, leading to conflicts between local optimization and global objectives; second, existing carbon emission models for high-energy-consuming industrial users have poor cross-industry compatibility, such as the steel industry carbon emission model cannot be directly transferred to cement users, lacking a universal carbon emission model; third, existing centralized optimization methods require sharing the privacy data (such as production plans) of high-energy-consuming industrial users, creating a bottleneck for privacy data leakage. Summary of the Invention

[0006] To address the aforementioned technical issues, a method for interactive operation between load aggregators and high-energy-consuming industrial users based on master-slave game theory is proposed. This method includes collecting load data required for interactive operation between load aggregators and high-energy-consuming industrial users at the beginning of the optimization cycle. Based on the collected data, carbon emission models and electricity consumption models for high-energy-consuming industrial users are established to calculate total carbon emissions and electricity consumption. Based on the results of carbon emissions and electricity consumption, we construct a revenue function for load aggregators to maximize aggregating revenue with the difference between electricity price and carbon price, and an operating cost function for high-energy-consuming industrial users to minimize production costs with internal electricity price and carbon price. The revenue function and operating cost function are constructed into a two-layer game model. The upper layer is set by the load aggregator to set internal electricity price and carbon price strategies, and the lower layer is set by high-energy-consuming industrial users to adjust their electricity consumption behavior and production plans based on the strategies. Using a two-level game model as the optimization objective, a game optimization method is adopted. Based on the optimal response feedback from high-energy-consuming industrial users, the load aggregator updates the strategy, and the process is repeated iteratively until the game equilibrium solution is reached.

[0007] As a preferred embodiment of the load aggregator and high-energy-consuming industrial user interaction operation method based on master-slave game theory described in this invention, the carbon emission model is calculated by multiplying the carbon emission factor and energy consumption, wherein the energy consumption includes fossil fuel consumption and externally purchased electricity; the electricity consumption model is calculated by multiplying the electricity consumption per unit output and the product output.

[0008] As a preferred embodiment of the load aggregator and high-energy-consuming industrial user interaction operation method based on master-slave game described in this invention, the revenue function includes the difference between the internal electricity sales price and the external electricity purchase price multiplied by the aggregated power load, and the difference between the internal carbon price and the external carbon price multiplied by the carbon emission rights purchase amount. The operating cost function includes the product of internal electricity price and electricity consumption, the product of internal carbon price and carbon emission rights purchase, and deducts product sales revenue.

[0009] As a preferred embodiment of the load aggregator and high-energy-consuming industrial user interaction operation method based on master-slave game theory described in this invention, the two-layer game model is characterized by the upper-layer strategy space being modeled as a piecewise linear combination of electricity price and carbon price, and the output decision of high-energy-consuming industrial users in the lower-layer response space being constrained by the total order amount, while their energy consumption is constrained by the maximum load.

[0010] As a preferred embodiment of the load aggregator and high-energy-consuming industrial user interaction operation method based on master-slave game theory described in this invention, the two-layer game model is a static master-slave game structure, in which the load aggregator, as the game leader, sets the strategy, and the high-energy-consuming industrial user, as the slave, optimizes its own response under the known strategy.

[0011] As a preferred embodiment of the master-slave game-based interactive operation method between load aggregators and high-energy-consuming industrial users described in this invention, the game optimization method is an iterative distributed solution method, in which each high-energy-consuming industrial user independently solves the optimal response locally and only feeds back its optimization results to the load aggregator, which then updates its pricing strategy and broadcasts the results.

[0012] As a preferred embodiment of the load aggregator and high-energy-consuming industrial user interaction operation method based on master-slave game described in this invention, the solution process terminates in any iteration when the change in internal price strategy is lower than the preset convergence threshold or the number of iterations reaches the set upper limit, and outputs the final internal electricity price, carbon price and the optimal response strategy of each high-energy-consuming industrial user.

[0013] As a preferred embodiment of the load aggregator and high-energy-consuming industrial user interactive operation system based on master-slave game theory described in this invention, the system is characterized by including: a data acquisition module, used to collect load, electricity price, and carbon price data required for the interactive operation of the load aggregator and high-energy-consuming industrial user; The interactive decision-making framework module is used to analyze the types of high-energy-consuming industrial users and establish an interactive operation framework between load aggregators and high-energy-consuming industrial users from the perspective of electricity-carbon synergy. The carbon emission accounting boundary establishment module is used to clarify the carbon emission accounting boundaries for different types of high-energy-consuming industrial users. A module for establishing generalized carbon emission and electricity consumption models is used to establish generalized carbon emission and electricity consumption models for different types of high-energy-consuming industrial users. The module for establishing equivalent electricity price and carbon price models is used to build equivalent electricity price and carbon price models for external markets from the perspective of electricity-carbon synergy. The economic model building module is used to construct the revenue model of the load aggregator and the cost model of a typical high-energy-consuming industrial user. The master-slave game model building module is used to build a master-slave game model between load aggregators and high-energy-consuming industrial users. The distributed solution module is used to find the equilibrium solution of the constructed master-slave game model; The simulation verification module performs simulation analysis and verification of the effectiveness of the optimization method.

[0014] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the described method for interactive operation between a load aggregator and a high-energy-consuming industrial user based on master-slave game theory.

[0015] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the aforementioned method for interactive operation between a load aggregator and a high-energy-consuming industrial user based on master-slave game theory.

[0016] The beneficial effects of this invention are as follows: By analyzing the types of high-energy-consuming industrial users, this invention constructs an interactive decision-making framework between load aggregators and high-energy-consuming industrial users from an electricity-carbon collaborative perspective. This framework supports collaborative optimization decisions among multiple stakeholders, including load aggregators and high-energy-consuming industrial users. It clarifies the carbon emission accounting boundary for high-energy-consuming industrial users, establishes a generalized carbon emission and electricity consumption model for them, and achieves unified modeling of carbon emissions and electricity consumption for different types of high-energy-consuming industrial users. It establishes equivalent electricity price and carbon price models, an economic model for load aggregators and high-energy-consuming industrial users, and proposes an interactive operation model and distributed solution method based on master-slave game theory between load aggregators and high-energy-consuming industrial users. This overcomes the problem of traditional centralized optimization operation failing to protect the data privacy of different stakeholders, enabling mutual benefit and win-win outcomes for different decision-making stakeholders. The interactive operation method between load aggregators and high-energy-consuming industrial users proposed in this invention can effectively aggregate the flexible adjustment capabilities of multiple types of high-energy-consuming industrial users, achieving mutual benefit and win-win outcomes for both load aggregators and high-energy-consuming industrial users, and has certain practical application value. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 The above is a flowchart of an overall operation method for interaction between load aggregators and high-energy-consuming industrial users based on master-slave game theory, provided as an embodiment of the present invention.

[0019] Figure 2 This is a system scheme block diagram of a load aggregator and high-energy-consuming industrial user interactive operation system based on master-slave game theory, provided as an embodiment of the present invention.

[0020] Figure 3 The LA revenue convergence curve of the load aggregator and high-energy-consuming industrial user interaction operation method based on master-slave game theory provided in one embodiment of the present invention.

[0021] Figure 4 The user cost convergence curve is shown in an embodiment of the present invention for a load aggregator and high-energy-consuming industrial user interaction operation method based on master-slave game theory. Detailed Implementation

[0022] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.

[0023] Example 1, referring to Figure 1 This is the first embodiment of the present invention, which provides a method for the interactive operation of load aggregators and high-energy-consuming industrial users based on master-slave game theory, including: At the start of the optimization cycle, data required for interaction and operation between load aggregators and high-energy-consuming industrial users is collected. Based on the collected data, carbon emission models and electricity consumption models for high-energy-consuming industrial users are established to calculate total carbon emissions and electricity consumption. Based on the results of carbon emissions and electricity consumption, we construct a revenue function for load aggregators to maximize aggregating revenue with the difference between electricity price and carbon price, and an operating cost function for high-energy-consuming industrial users to minimize production costs with internal electricity price and carbon price. The revenue function and operating cost function are constructed into a two-layer game model. The upper layer is set by the load aggregator to set internal electricity price and carbon price strategies, and the lower layer is set by high-energy-consuming industrial users to adjust their electricity consumption behavior and production plans based on the strategies. In a preferred embodiment of the present invention, the two-layer game model is constructed as a master-slave game structure. The load aggregator, as the master, sets internal electricity and carbon price strategies at the start of the operating cycle; high-energy-consuming industrial users, as slaves, optimize their electricity consumption behavior and production plans under these strategies. The strategies of both parties interact, forming a coupled game structure between the upper and lower layers, and the system operation is optimized with the goal of achieving game equilibrium. In this embodiment, the strategies selected by the game participants satisfy the optimization of their respective objective functions and do not violate external price constraints and quota conditions.

[0024] The beneficial effects of this preferred technical solution are as follows: by modeling the interaction between load aggregators and high-energy-consuming industrial users as a master-slave game model with a clear hierarchical structure, the electricity price and carbon price strategies can dynamically guide user behavior under the global optimization framework, avoiding the local optimum problem caused by the electricity-carbon segmentation in traditional phased optimization, and significantly improving the multi-entity operation coordination efficiency and market adaptability.

[0025] In an optional embodiment of the present invention, the two-layer game model is extended to a multi-agent parallel game structure. In this embodiment, more than one load aggregator participates in the upper-level strategy formulation, with different aggregators providing electricity and carbon price signals to their respective high-energy-consuming industrial users; lower-level users independently optimize their responses according to their respective access strategies to achieve locally optimal behavior. In this structure, the linkage control problem between multiple regions or multiple dispatch centers can be solved through parallel computing.

[0026] An iterative distributed solution method is adopted, with feedback responses from high-energy-consuming industrial users. The load aggregator updates its strategy accordingly, and the process is repeated until the system converges to a game equilibrium solution.

[0027] In a preferred embodiment of the present invention, the game optimization method employs an iterative distributed solution strategy. In each iteration, high-energy-consuming industrial users independently solve their operating cost minimization problem locally and only feed back their optimization results (such as electricity demand and carbon emission rights demand) to the load aggregator. The load aggregator aggregates all user responses, adjusts its electricity and carbon pricing strategies, and broadcasts them to all users to enter the next iteration. The above process is repeated until the price change is less than a set convergence threshold or the maximum number of iterations is reached, thereby obtaining the game equilibrium solution under the electricity-carbon synergy condition.

[0028] The beneficial effects of this preferred technical solution are as follows: by adopting an iterative distributed solution method, each decision-making entity only needs to perform independent optimization and exchange limited response information locally, which reduces the need for centralized transmission of sensitive production data of industrial users, effectively reduces the risk of privacy leakage, and improves the computational scalability and practical deployment feasibility of the model in large-scale user scenarios.

[0029] In an optional embodiment of the present invention, the game-theoretic optimization method employs a centralized solution strategy. A load aggregator centrally collects basic data from all high-energy-consuming industrial users, including production plans, electricity load, and emission intensity. Based on the known model structure, the two-level game problem is transformed into a single-level optimization problem. The subordinate optimization problem is then embedded into the dominant objective function using KKT (Karush-Kuhn-Tucker) conditions, forming an equivalent constraint structure. The Lagrange multiplier method is then used to solve the problem, obtaining the overall optimal solution for the system. This approach is suitable for operational scenarios with high requirements for information control and high data centralization.

[0030] This invention aims to address three core problems in existing technologies: fragmented electricity-carbon collaborative optimization for high-energy-consuming industrial users, insufficient universality of carbon emission modeling, and multi-agent collaborative optimization decision-making. Specifically, the objectives include: establishing an interactive decision-making framework between load aggregators and high-energy-consuming industrial users from an electricity-carbon collaborative perspective by introducing load aggregators; studying the carbon emission accounting boundary of high-energy-consuming industrial users and establishing a universal carbon emission and electricity consumption model for them; constructing an interactive operation model between load aggregators and high-energy-consuming industrial users based on master-slave game theory, and designing a distributed algorithm for solving the game equilibrium solution to achieve collaborative decision-making and optimization for different decision-making agents.

[0031] It should be noted that load aggregators (LAs) are emerging market participants that can integrate the demand response capabilities of multiple users to provide more flexible and efficient energy regulation and trading services.

[0032] High-energy-consuming industrial users refer to industrial users whose energy consumption is relatively large and accounts for a high proportion of their business activities. This invention focuses on four typical industrial users: steel, electrolytic aluminum, copper smelting, and cement.

[0033] Master-slave game is an important form of game theory, mainly involving two roles: the leader and the follower. The leader first formulates a strategy, while the follower chooses their own action strategy based on the leader's strategy to achieve the optimal outcome.

[0034] Compared with existing technologies, this invention has three significant differences: First, in terms of the optimized operating framework, unlike existing electricity-carbon separation solutions, this invention constructs an interactive decision-making framework between load aggregators and high-energy-consuming industrial users from an electricity-carbon collaborative perspective; second, in terms of carbon emission modeling, addressing the shortcomings of existing models in terms of generalization, this invention studies the carbon emission accounting boundary of high-energy-consuming industrial users and establishes a generalized carbon emission model applicable to different types of high-energy-consuming industrial users; third, in terms of multi-agent interaction, addressing the shortcomings of existing centralized optimization methods in balancing the optimization needs of different agents and data privacy, this invention introduces master-slave game theory, with load aggregators as leaders and high-energy-consuming industrial users as followers, constructing a multi-agent game interaction operating model between load aggregators and high-energy-consuming industrial users, and designing a distributed solution algorithm to protect user data privacy.

[0035] By employing the master-slave game-based interactive operation method between load aggregators and high-energy-consuming industrial users described in this invention, the optimized results can be applied to the operational decisions of actual high-energy-consuming industrial users participating in the electricity-carbon market. The basic data upon which the interactive operation method in this invention is based includes electricity load, electricity price, and carbon price data for high-energy-consuming industries. The decision-making entities participating in the interactive operation include load aggregators and typical high-energy-consuming industrial users, which aligns with the actual development of the distribution network and the electricity-carbon market. Considering the differentiated carbon emissions and electricity consumption characteristics of different types of high-energy-consuming industrial users, the proposed generalized carbon emission and electricity consumption model is applicable to different types of high-energy-consuming industrial users. The proposed master-slave game-based interactive operation model and distributed solution algorithm can be used to describe the collaborative optimization problem between different decision-making entities of load aggregators and high-energy-consuming industrial users, and can overcome the privacy data leakage problem in the traditional centralized solution process. By using the interactive operation method described in this invention, the optimization results can be applied to the interactive operation decision-making between load aggregators and high-energy-consuming industrial users in the electricity-carbon joint market environment. By rationally arranging the electricity consumption and carbon emission rights purchase plans of different types of high-energy-consuming industrial users, a win-win situation can be achieved between load aggregators and high-energy-consuming industrial users, which plays a positive role in promoting the consumption and utilization of new energy sources and helping to achieve dual-carbon goals.

[0036] Example 2, refer to Figure 1 The second embodiment of the present invention provides a method for the interactive operation of load aggregators and high-energy-consuming industrial users based on master-slave game theory, including: Step 100: Collect data such as load, electricity price, and carbon price required for the interaction and operation between load aggregators and high-energy-consuming industrial users. These data are input data, which are external data that load aggregators and high-energy-consuming industrial users need to input. Other parameters in the model / formula are considered internal data and are given parameters during the modeling and solving process. Step 200: Establish an interactive decision-making framework between load aggregators and high-energy-consuming industrial users from the perspective of electricity-carbon synergy; Step 300: Define the carbon emission accounting boundaries for high-energy-consuming industrial users; Step 400: Establish a generalized carbon emission and electricity consumption model for high-energy-consuming industrial users; Step 500: Establish an equivalent electricity price and carbon price model; Step 600: Establish an economic model for load aggregators and high-energy-consuming industrial users; Step 700: Establish an interactive operation model between load aggregators and high-energy-consuming industrial users based on master-slave game theory.

[0037] Step 800: Design a distributed algorithm for finding the equilibrium solution of the game.

[0038] Step 200: Establishing an interactive decision-making framework between load aggregators and high-energy-consuming industrial users from the perspective of electricity-carbon synergy, specifically including: Typical high-energy-consuming industrial users include steel, electrolytic aluminum, copper smelting, and cement. From the perspective of electricity-carbon synergy, both load aggregators and high-energy-consuming industrial users are independent entities with autonomous decision-making capabilities, and their respective decisions affect each other's revenue or costs. Specifically, load aggregators are responsible for supplying electricity to various types of high-energy-consuming industrial users within the region, maximizing their own revenue by setting internal electricity and carbon prices; high-energy-consuming industrial users adjust their electricity consumption behavior based on the electricity and carbon prices provided by the load aggregator to minimize their own electricity costs.

[0039] It should be noted that this step is used to establish an interactive decision-making framework. The framework supports load aggregators in supplying electricity to various types of high-energy-consuming industrial users within the region, maximizing their own revenue by setting internal electricity and carbon prices. High-energy-consuming industrial users adjust their electricity consumption behavior based on the electricity and carbon prices provided by the load aggregators to minimize their own electricity costs.

[0040] Step 300: Defining the carbon emission accounting boundaries for high-energy-consuming industrial users, specifically including: The emissions from high-energy-consuming production enterprises mainly involve four parts: primary energy use, secondary energy use, industrial production process emissions, and carbon sequestration product emissions. Primary energy use includes emissions from fossil fuel combustion and the use of energy as a raw material. Secondary energy use includes emissions from the net purchase and use of electricity and heat. In addition, a small portion of carbon generated during the production process is sequestered in carbon sequestration products produced in the process or from raw materials; the carbon emissions corresponding to this portion of carbon sequestered in the products can be deducted.

[0041] Taking steel production enterprises as an example, their carbon emissions and accounting boundaries include four aspects: fossil fuel combustion, industrial production processes, net purchase and use of electricity and heat, and carbon emissions implied by carbon sequestration products. The processes included in the accounting include coking, sintering, ironmaking, and steelmaking, etc. The accounting boundary for each process starts from the entry of raw materials and energy into the process and ends at the exit of the final product and by-products.

[0042] Step 400: Establishing a generalized carbon emission and electricity consumption model for high-energy-consuming industrial users, specifically including: Generalized carbon emission model: The total carbon dioxide emissions of high-energy-consuming industries equal the sum of emissions from primary energy use, secondary energy use, and industrial production processes within the enterprise boundary, and should also deduct the emissions implicit in carbon sequestration products. The calculation is performed using the following formula: Where n is the index of high-energy-consuming industrial users. This represents the total carbon dioxide emissions of the nth type of high-energy-consuming industry. , , , These represent emissions from primary energy use, secondary energy use, industrial production processes, and carbon sequestration products, respectively. Specifically, emissions from primary energy use mainly include carbon emissions from fossil fuel combustion and the use of energy as a raw material. Therefore, the carbon emissions from primary energy use, secondary energy use, and industrial production processes of the nth type of high-energy-consuming industry can be represented in the following general form: Where k is an index of carbon emission sources, including four categories: primary energy use, secondary energy use, industrial production processes, and carbon sequestration products. This represents the consumption / production of carbon emission sources of category k in category n high-energy-consuming industries during the carbon accounting period. This represents the corresponding carbon emission factor.

[0043] Generalized energy consumption model: High-energy-consuming manufacturing enterprises consume a large amount of electricity during the production process. To accurately measure their electricity consumption level, the electricity consumption per ton of product is used as an indicator to characterize the total electricity consumption intensity. in, and These represent the electricity consumption and corresponding product output of the nth type of high-energy-consuming industry during time period t, respectively. For the nth type of high-energy-consuming industry, the electricity consumption per ton of product is derived from statistical data. Taking steel production enterprises as an example, the electricity consumption per ton of steel production enterprises ranges from 400 to 700 kWh / t. Among them, large steel plants can usually achieve lower electricity consumption per ton of steel due to their economies of scale and technological advantages.

[0044] Step 500: Establishing an equivalent electricity price and carbon price model, specifically including: Equivalent electricity price model: Since the marginal cost of power generation is a linear function of power generation, the external market electricity price is assumed to be a piecewise linear function of the total power load, as calculated below: in, and Representing the external electricity price and the total system power load during time period t, respectively. express A piecewise linear function; For other loads within the electricity market during time period t, The aggregated power load is the load aggregator for time period t.

[0045] Equivalent carbon price model: Carbon price can be approximated by a Cournot model based on quantity competition, and the carbon price at time t can be expressed as: in, and These represent the external carbon price and the total system carbon emission rights purchase demand during time period t, respectively. , It is a parameter in the inverse price function of the Cournot game model, and its value is positive; A ceiling on the carbon market clearing price set by regulatory authorities; This is for the carbon emission rights purchase needs of other entities during period t. This is to meet the demand for carbon emission rights purchases by load aggregators.

[0046] Step 600: Establishing an economic model for load aggregators and high-energy-consuming industrial users, specifically including: Economic model of load aggregator: As the leader in a master-slave game, the load aggregator guides high-energy-consuming industrial users to adjust their electricity consumption behavior by setting optimal electricity prices and carbon prices for each time period within the operating cycle, thereby maximizing overall benefits. The total revenue of the load aggregator can be expressed as: in, This represents the revenue of the load aggregator during time period t, including the revenue from electricity during time period t. and carbon emission benefits ; and These represent the electricity price and carbon price within time period t set by the load aggregator, respectively. and Let represent the external electricity price and carbon price during time period t, respectively; and These represent the aggregated electricity load and carbon emission rights purchase demand of the load aggregator during time period t, respectively. A collection of high-energy-consuming industrial users and This refers to the electricity consumption and carbon emission rights purchase needs of high-energy-consuming industrial users n during period t. Carbon allowances allocated to high-energy-consuming industrial users n during time period t. Let t represent the total carbon emissions of high-energy-consuming industrial users n during the time period t.

[0047] The decision variable for load aggregators is the internal electricity price. and carbon price The following constraints must be met: in, and These are the upper and lower limits of the internal electricity price for load aggregators; and These represent the upper and lower limits of the internal carbon price for load aggregators.

[0048] Economic model of high energy-consuming industries: High-energy-consuming industrial users, acting as subordinates in a master-slave game, minimize their electricity costs by adjusting their electricity consumption behavior based on the internal electricity and carbon prices set by load aggregators. The operating costs of high-energy-consuming industrial users can be expressed as: in, This represents the operating cost of the nth type of high-energy-consuming industrial user during time period t. For the electricity revenue of the nth type of high-energy-consuming industrial users; Let t represent the output of the nth type of high-energy-consuming industry. For the unit product revenue of the nth type of high-energy-consuming industry; This represents the original production plan of the nth type of high-energy-consuming industrial user during time period t. This is a penalty factor.

[0049] During the optimization period, high-energy-consuming industrial users must meet the following constraints when producing products: in, To optimize the set of time periods within the period, This represents the upper limit of the production rate for the nth type of high-energy-consuming industrial user. To optimize the total number of product orders within the cycle; and These represent the electricity load and the upper limit of carbon emission rights purchase demand for the nth type of high-energy-consuming industrial user during time period t.

[0050] Step 700: Establishing an interactive operation model between load aggregators and high-energy-consuming industrial users based on master-slave game theory, specifically including: Construct a master-slave game model between load aggregators and high-energy-consuming industrial users: In this context, LA represents the game leader. Represents the set of game participants; and These are the strategies of the game leader. and The strategy of the subordinate in the game; and Let represent the payment functions for the load aggregator and the high-energy-consuming industry n, respectively; In the above game theory model, the following conditions must be met for each player's strategy to succeed: Then the strategy set It is the equilibrium solution of the master-slave game model.

[0051] Step 800: Propose a distributed algorithm for finding the equilibrium solution of the game, specifically including: The master-slave game model constructed above can be described as a two-layer optimization architecture, typically solved using KKT transformation and a centralized approach. However, load aggregators and high-energy-consuming industrial users are independent decision-making entities, and centralized solutions pose a data privacy leakage problem. Therefore, this study designs an iterative distributed solution algorithm. During the solution process, load aggregators and high-energy-consuming industrial users only need to interact with limited information, greatly protecting the data privacy of the decision-making entities. The specific algorithm implementation steps are as follows: First, the master-slave game model is initialized, including initializing the convergence error coefficient, the maximum number of iterations, and the initial electricity and carbon prices. Second, high-energy-consuming industrial users solve the cost minimization optimization problem based on internal electricity and carbon price information to obtain their respective electricity consumption strategies and feed them back to the load aggregator. Then, the load aggregator solves the profit maximization problem based on the electricity consumption strategies of each high-energy-consuming industrial user to adjust the internal electricity and carbon price signals. This process is repeated until the convergence condition is met (reaching the maximum number of iterations or the objective function value of two adjacent iterations is less than the convergence error coefficient), i.e., reaching the maximum number of iterations or the objective function value of two adjacent iterations is less than the convergence error coefficient.

[0052] Example 3, referring to Figure 3 and Figure 4 This is the third embodiment of the present invention. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0053] To verify the effectiveness of the aforementioned technology, a case study analysis was conducted using one load aggregator and four typical industrial users (including steel, electrolytic aluminum, copper smelting, and cement) as research objects. The internal carbon price ceiling was set at 50 yuan / t, and the floor at 10 yuan / t; the electricity price ceiling was set at 1.5 yuan / kWh, and the floor at 0.5 yuan / kWh; the external market electricity load data was generated by anonymizing and modifying data from a certain region in Yunnan Province. The electricity consumption per ton of product for steel, electrolytic aluminum, copper smelting, and cement was set at 500 kWh / t, 210.83 kWh / t, 12828.2 kWh / t, and 1000 kWh / t, respectively. The revenue from the sale of finished products after deducting raw material costs was set at 547 yuan / t, 30000 yuan / t, 3000 yuan / t, and 280 yuan / t, respectively.

[0054] To verify the effectiveness of the proposed iterative distributed solution method, a comparison with a centralized solution algorithm was conducted, and the convergence curve of the proposed distributed solution method was obtained as follows: Figure 3 and Figure 4 As shown in the figure. For the differential evolution algorithm used, the crossover probability and differential weight system are set to 0.8 and 0.85, respectively, and the maximum number of iterations is set to 100. It is easy to see that as the number of iterations increases, the return of LA continuously increases, eventually stabilizing after 35 iterations, and the return of LA approaches the result of centralized solution; the user's running cost decreases with the increase of the number of iterations, and also converges after 35 iterations, thus proving that the proposed iterative solution algorithm has stable convergence performance.

[0055] To further investigate the impact of the master-slave game optimization framework on the strategy selection, efficiency improvement, or cost reduction of LA and high-energy-consuming industrial users in the context of the combined electricity-carbon market, four different test scenarios were designed.

[0056] Scenario S0: High-energy-consuming industrial users do not participate in the carbon electricity market.

[0057] Scenario S1: An interactive operation scenario based on master-slave game theory in a combined electricity-carbon market environment. In this scenario, the LA and the user have a master-slave game relationship and participate in both the electricity market and the carbon market.

[0058] Scenario S2: An interactive operation scenario based on master-slave game theory in an electricity market environment. In this scenario, LA and users have a master-slave game relationship, but the carbon trading price is fixed, and only the operation decisions of the electricity market are considered.

[0059] Scenario S3: Integrated operation of LA and high-energy-consuming industrial users in a combined electricity-carbon market environment. This scenario considers the participation of LA and users in electricity and carbon market decision-making, but adopts a centralized operation strategy and does not consider the game relationship between LA and users.

[0060] The simulation results are shown in Table 1. It can be concluded that: 1) participation in the electric carbon market by high-energy-consuming industrial users can effectively reduce their own operating costs; 2) participation in the electric carbon market can simultaneously improve the economics of aggregators and users; 3) the proposed master-slave game theory method can effectively model the interactive behavior among multiple subjects.

[0061] Table 1 Comparison of optimization results in different scenarios

[0062] Example 4 is the fourth embodiment of the present invention, which differs from the previous three embodiments in that: If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0063] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0064] More specific examples of computer-readable media (a non-exhaustive list) include: electrical connections (electronic devices) having one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0065] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented in combination with any of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0066] Example 5, refer to Figure 2 The fifth embodiment of the present invention provides a load aggregator and high-energy-consuming industrial user interaction operation system based on master-slave game theory, comprising: The data acquisition module is used to collect load, electricity price, and carbon price data required for the interaction and operation between load aggregators and high-energy-consuming industrial users. The interactive decision-making framework module is used to analyze the types of high-energy-consuming industrial users and establish an interactive operation framework between load aggregators and high-energy-consuming industrial users from the perspective of electricity-carbon synergy. The carbon emission accounting boundary establishment module is used to clarify the carbon emission accounting boundaries for different types of high-energy-consuming industrial users. A module for establishing generalized carbon emission and electricity consumption models is used to establish generalized carbon emission and electricity consumption models for different types of high-energy-consuming industrial users. The module for establishing equivalent electricity price and carbon price models is used to build equivalent electricity price and carbon price models for external markets from the perspective of electricity-carbon synergy. The economic model building module is used to construct the revenue model of the load aggregator and the cost model of a typical high-energy-consuming industrial user. The master-slave game model building module is used to build a master-slave game model between load aggregators and high-energy-consuming industrial users. The distributed solution module is used to find the equilibrium solution of the constructed master-slave game model; The simulation verification module performs simulation analysis and verification of the effectiveness of the optimization method.

[0067] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for interactive operation between load aggregators and high-energy-consuming industrial users based on master-slave game theory, characterized by: include, At the start of the optimization cycle, load data required for interaction and operation between load aggregators and high-energy-consuming industrial users is collected. Based on the collected data, carbon emission models and electricity consumption models for high-energy-consuming industrial users are established to calculate total carbon emissions and electricity consumption. Based on the results of carbon emissions and electricity consumption, we construct a revenue function for load aggregators to maximize aggregating revenue with the difference between electricity price and carbon price, and an operating cost function for high-energy-consuming industrial users to minimize production costs with internal electricity price and carbon price. The revenue function and operating cost function are constructed into a two-layer game model. The upper layer is set by the load aggregator to set internal electricity price and carbon price strategies, and the lower layer is set by high-energy-consuming industrial users to adjust their electricity consumption behavior and production plans based on the strategies. Using a two-level game model as the optimization objective, a game optimization method is adopted. Based on the optimal response feedback from high-energy-consuming industrial users, the load aggregator updates the strategy, and the process is repeated iteratively until the game equilibrium solution is reached.

2. The method for interactive operation between load aggregators and high-energy-consuming industrial users based on master-slave game theory as described in claim 1, characterized in that: The carbon emission model is calculated by multiplying the carbon emission factor by the energy consumption, which includes fossil fuel consumption and externally purchased electricity; the electricity consumption model is calculated by multiplying the electricity consumption per unit output by the product output.

3. The method for interactive operation between load aggregators and high-energy-consuming industrial users based on master-slave game theory as described in claim 2, characterized in that: The revenue function includes the difference between the internal electricity sales price and the external electricity purchase price multiplied by the aggregated electricity load, and the difference between the internal carbon price and the external carbon price multiplied by the amount of carbon emission rights purchased. The operating cost function includes the product of internal electricity price and electricity consumption, the product of internal carbon price and carbon emission rights purchase, and deducts product sales revenue.

4. The method for interactive operation between load aggregators and high-energy-consuming industrial users based on master-slave game theory as described in claim 3, characterized in that: In the two-layer game model, the upper-layer strategy space is modeled as a piecewise linear combination of electricity price and carbon price, while the lower-layer response space shows that the output decisions of high-energy-consuming industrial users are constrained by the total order volume, and their electricity consumption is constrained by the maximum load.

5. The method for interactive operation between load aggregators and high-energy-consuming industrial users based on master-slave game theory as described in claim 4, characterized in that: The two-layer game model is a static master-slave game structure, in which the load aggregator, as the dominant party, sets the strategy, and the high-energy-consuming industrial user, as the subordinate party, optimizes its own response under the known strategy.

6. The method for interactive operation between load aggregators and high-energy-consuming industrial users based on master-slave game theory as described in claim 5, characterized in that: The game optimization method described is an iterative distributed solution method, in which each high-energy-consuming industrial user independently solves for the optimal response locally and only reports its optimization results to the load aggregator. The load aggregator then updates its pricing strategy and broadcasts the results accordingly.

7. The method for interactive operation between load aggregators and high-energy-consuming industrial users based on master-slave game theory as described in claim 6, characterized in that: The solution process terminates in any iteration when the change in the internal price strategy is lower than the preset convergence threshold or the number of iterations reaches the set upper limit, and outputs the final internal electricity price, carbon price, and the optimal response strategy for each high-energy-consuming industrial user.

8. A load aggregator and high-energy-consuming industrial user interaction operation system based on master-slave game theory, employing the load aggregator and high-energy-consuming industrial user interaction operation method based on any one of claims 1 to 7, characterized in that, include: The data acquisition module is used to collect load, electricity price, and carbon price data required for the interaction and operation between load aggregators and high-energy-consuming industrial users. The interactive decision-making framework module is used to analyze the types of high-energy-consuming industrial users and establish an interactive operation framework between load aggregators and high-energy-consuming industrial users from the perspective of electricity-carbon synergy. The carbon emission accounting boundary establishment module is used to clarify the carbon emission accounting boundaries for different types of high-energy-consuming industrial users. A module for establishing generalized carbon emission and electricity consumption models is used to establish generalized carbon emission and electricity consumption models for different types of high-energy-consuming industrial users. The module for establishing equivalent electricity price and carbon price models is used to build equivalent electricity price and carbon price models for external markets from the perspective of electricity-carbon synergy. The economic model building module is used to construct the revenue model of the load aggregator and the cost model of a typical high-energy-consuming industrial user. The master-slave game model building module is used to build a master-slave game model between load aggregators and high-energy-consuming industrial users. The distributed solution module is used to find the equilibrium solution of the constructed master-slave game model; The simulation verification module performs simulation analysis and verification of the effectiveness of the optimization method.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for interactive operation between load aggregators and high-energy-consuming industrial users based on master-slave game theory as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method for interactive operation between load aggregators and high-energy-consuming industrial users based on master-slave game theory as described in any one of claims 1 to 7.

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