Local area network and main network global optimal operation method, device and equipment based on asymmetric Nash negotiation and storage medium

Abstract

The invention provides a local area network and main network global optimal operation method and device based on asymmetric Nash negotiation, equipment and a storage medium, and relates to the technical field of power grids. The method comprises the steps of performing interaction characteristic analysis of different operation modes of a local area network according to functional characteristics and structural characteristics of different types of local area power grids, establishing a model for coordinated and optimized operation of the different types of local area power grids and a large power grid, and fully considering product quality, yield and demand response characteristics of an electric smelting magnesium enterprise; constructing a coordinated optimization operation model of a local power grid and a large power grid with optimal overall operation cost of the system as a target, and transacting electric energy transmission among the power grids according to respective interaction cost coefficients so as to achieve global optimization of the whole system; a benefit distribution model among multiple subjects is constructed based on a Nash negotiation theory, and a comprehensive contribution rate is introduced to quantify the bargaining ability of each subject, so that the contribution of each subject is compensated, and the fairness of benefit distribution is improved.

Description

Technical Field

[0001] This application relates to the field of power grid technology, and in particular to a method, apparatus, equipment and storage medium for global optimal operation of local area network and main network based on asymmetric Nash negotiation. Background Technology

[0002] Integrated power generation, grid, load, and storage local power grids typically include their own distributed power sources, energy storage, flexible loads, and certain industrial loads within the region. Based on their characteristics, they can be categorized as industrial, residential, or commercial. Each type of industrial park can achieve operational stability and economic efficiency through its internal flexible resources. However, each type of industrial park operates in its own optimal mode, which not only increases the burden on the main grid but also fails to maximize social welfare and achieve overall optimization. Therefore, how to effectively utilize the flexible resources within different local power grids to achieve complementarity and mutual support, reduce flexibility demands detrimental to the main grid's operation, and achieve overall optimization including the main grid is a problem that urgently needs to be solved.

[0003] In recent years, existing technologies have been used to conduct extensive research on the interconnection and operation of different local power grids. For example, the paper "Wu Jinling, Lou Ping, Guan Minyuan, et al. Optimization strategy for multi-microgrid power sharing operation based on asymmetric Nash negotiation [J]. Power System Technology, 2022, 46(7): 2711-2723." uses asymmetric negotiation to achieve power sharing and benefit distribution among multiple microgrids. The paper "Li Peng, Wu Difan, Li Yuwei, et al. Optimization scheduling strategy for multi-microgrid integrated energy system based on integrated demand response and master-slave game [J]. Proceedings of the CSEE, 2021, 41(4): 1307-1321." uses master-slave game theory to establish a multi-microgrid coordinated scheduling model that considers various demand responses. The literature “Cheng Shan, Chen Ziming, Wang Rui, et al. Two-layer coordinated optimization scheduling of multi-microgrids based on hybrid game theory [J]. Electric Power Automation Equipment, 2021, 41(8).” proposes a two-layer optimization model for multi-microgrid systems based on hybrid game theory. The literature “Chu Zhuang, Li Qiuyu, Wang Yijian. Electric-heat-carbon optimization scheduling strategy for interconnected heterogeneous multi-microgrid systems based on non-zero-sum game theory [J / OL]. Power System Technology: 1-13 [2024-05-15]” proposes an optimization scheduling model for interconnected heterogeneous multi-microgrids based on the idea of ​​non-zero-sum game theory. The literature “Gu Xin, Wang Qi, Hu Yunlong, et al. Distributed low-carbon optimization operation strategy for integrated energy systems of multi-microgrids based on Nash bargaining [J]. Power System Technology, 2022, 46(4): 1464-1482.” constructs a cooperative operation model for multi-microgrids considering low-carbon emission reduction based on Nash bargaining theory. The paper "Luo Ping, Zhou Haobing, Xu Lin. Day-ahead Optimization Scheduling of Multi-Microgrid with Combined Cooling, Heating and Power Based on Interval Optimization [J]. Automation of Electric Power Systems, 2022, 46(9): 137-146." proposes a multi-local area network cooling-heating-power coordination optimization method based on interval optimization. The paper "Li Xue, Ji Hansong, Zhang Rufeng, et al. Distributed Optimization Scheduling of Urban Distribution Systems Considering the Response Characteristics of Multiple Types of Microgrids [J]. Automation of Electric Power Systems, 2022, 46(17): 74-82." proposes a distributed optimization scheduling method for different types of microgrids. The literature "Du Jianan, Han Xiaoqing, Li Tingjun, et al. Optimization strategy for multi-microgrid power cooperative operation considering electricity price uncertainty and game-theoretic fraud behavior [J]. Power System Technology, 2022, 46(11): 4217-4230." proposes a multi-microgrid cooperative operation based on Nash negotiation to address the uncertainty of electricity prices and microgrid transactions. The above studies mainly focus on the local area network itself, only considering the economy and reliability of the local power grid, without considering the impact on the safe and stable operation of the upper-level large power grid, or the impact on the economy of the main grid operation.

[0004] To further achieve flexible timing matching between local power grids and the main grid, utilizing load demand response in different types of local power grids to participate in system flexibility regulation has become a new approach to solving the problem. The literature "Tan Z, Yang P, Nehorai A. An optimal and distributed demand response strategy with electric vehicles in the smart grid[J]. IEEE Transactions on Smart Grid, 2014, 5(2):861-869." and "Liu Weijia, Wen Fushuan, Xue Yusheng, et al. Negotiation strategy for electric vehicles participating in power system optimal dispatch[J]. Automation of Electric Power Systems, 2015, 17." treats electric vehicles as a flexible power load participating in power system dispatch. The paper "Mi Yang, Li Zhanqiang, Wu Yanwei, et al. Two-level optimal scheduling of grid-connected microgrids based on two-level demand response [J]. Power System Technology, 2018, 42(6): 1899-1906." proposes a two-level optimal scheduling model based on load demand response, which regulates the demand-side load curve to make it match the output of renewable resources. The paper "Zhu Weiye, Luo Yi, Hu Bo, et al. Coordinated promotion of carbon emission reduction by thermal load elasticity and time-of-use electricity price demand-side response [J]. Power System Technology, 2021, 45(10): 3803-3813." constructs an optimal scheduling model for thermal load based on thermal load elasticity and time-of-use electricity price demand response, effectively including thermal load in the flexible resources. Compared with the above-mentioned demand responses, high energy-consuming loads have the characteristics of high energy consumption, high automation, and easy control, which means they have greater potential. The literature "Liu Chuang, Sun Ao, Wang Yibo, et al. Day-day and intraday joint economic dispatch method for power system considering the joint peak regulation of fused magnesium load and energy storage [J]. Electric Power Automation Equipment, 2022, 42(2):8-15." considers the limitations of production processes for high energy-consuming loads and establishes a day-day and intraday joint economic dispatch method for high energy-consuming loads and energy storage. The literature "Zhang Hailiang, Wang Yibo, Cai Guowei, et al. Source-load coordination optimization strategy for wind power consumption and fused magnesium high energy-consuming load regulation [J]. Journal of Electrical Engineering, 2022, 37(17): 4401-4410." considers the operating characteristics of fused magnesium high energy-consuming loads and establishes a source-load coordination planning method for wind power consumption and fused magnesium load regulation. The above studies usually only consider the role of load demand response within the local power grid and do not fully consider its role in the overall composition of local power grids with different functions and the main grid. Summary of the Invention

[0005] This application provides a method, apparatus, device, and storage medium for globally optimal operation of a local area network (LAN) and the main grid based on asymmetric Nash negotiation. Addressing the operational burden and unfair distribution of benefits caused by LANs operating in their own optimal mode, it proposes globally optimal operation strategies for different types of LANs and the main grid based on asymmetric Nash negotiation. First, a comparison of different operating modes of the LAN power grid is conducted, providing a theoretical foundation for subsequent implementation. Second, a coordinated optimization scheduling model for the LAN and the main grid, aiming at global optimization, is constructed. Benefit distribution is based on the comprehensive contribution rate of each entity as its bargaining power, fully mobilizing the initiative and enthusiasm of each entity. Finally, by comparing the impact of different schemes on the flexibility and operating costs of the main grid, the effectiveness and economy of the proposed scheme are demonstrated.

[0006] Firstly, this application provides a method for globally optimal operation of a local area network and a main network based on asymmetric Nash negotiation, including: Based on the functional characteristics and structural features of the local power grid, the types of local power grids are determined, and the interaction characteristics of different types of local power grids under various operating modes are analyzed; wherein, the types of local power grids include residential areas, high-energy-consuming industrial areas, and commercial areas; A coordinated optimization operation model for local power grids and the main grid is constructed. The coordinated optimization operation model aims to optimize the overall system operating cost, and the power transactions between local power grids and between local power grids and the main grid are based on the interaction cost coefficient. Based on the aforementioned coordinated optimization operation model, the optimal interactive power between local power grids and between local power grids and the main grid is calculated, and the globally optimal operation scheme is determined. A multi-stakeholder benefit allocation model is constructed based on asymmetric Nash negotiation theory. The multi-stakeholders include local power grids and the main grid. The multi-stakeholder benefit allocation model allocates benefits by quantifying the bargaining power of each stakeholder through the introduction of a comprehensive contribution rate. The comprehensive contribution rate is determined based on a nonlinear energy sharing mapping contribution factor and a demand response contribution factor. Based on the multi-entity benefit distribution model and the global optimal operation scheme, the operating costs of each entity are compensated, and the operation strategies of the local power grid and the main grid are adjusted.

[0007] In one possible design, the operating costs of each entity in the local power grid and the main power grid are determined using the following formula in the coordinated optimization operation model. C i : (1) In the formula, C i Representing the operating costs of different entities, i =1 represents a residential area. i=2 represents a high-energy-consuming industrial zone. i =3 represents the main network. If the main network does not contain this cost, then this item is zero. C lan Represents the cost of local area network mutual assistance. C buy This represents the cost of purchasing electricity from the main power grid by the local area network. Represents the revenue from electricity sales from the main power grid to the local area network. C gas Represents the cost of purchasing gas. C tran Represents the cost of transferable loads. C cut This means that load costs can be reduced. C g Represents the cost of thermal power units. C E Represents the operating and maintenance costs of energy storage charging and discharging. C a Represents the cost of wind and solar power curtailment in the system; in: Local area network mutual assistance costs C lan The calculation formula is: (2) In the formula, γ e This refers to the cost coefficient for energy sharing between local power grids. For local power grid i exist t Constantly interacting with the local power grid j The amount of electricity, P e ij,t For local power grid j exist t Constantly interacting with the local power grid i The amount of electricity, The time interval step size, T Total runtime; Local area network electricity purchase cost from the main power grid C buy The calculation formula is: (3) In the formula, γ buy ( t )for t The electricity price that the local area network purchases from the main grid during certain time periods. P buy i,t For local power grid i exist t Electricity purchased from the main power grid at all times.

[0008] Revenue from electricity sales from the main power grid to the local area network C 3. The formula for calculating sell is formula (4) or formula (5): (4) (5) Gas purchase cost C gas The calculation formula is: (6) In the formula, γ gas ( t )for t Gas price during specific time periods; For local power grid i Internal combined heat and power units t Gas consumption at any given moment; For local power grid i Internal gas boilers t Gas consumption at any time Transferable load cost C tran The calculation formula is: (7) In the formula, γ tran This is the compensation coefficient for the system's transferable load; For local power grid i Internal transferable load in t The power of time Reduced load costs C cut The calculation formula is: (8) In the formula, γ cut The compensation factor for load reduction in the system; For local power grid i Internal load reduction t Power at time thermal power unit cost C g The calculation formula is: (9) In the formula, γ g This is the operating cost coefficient for thermal power units; For local power grid i Internal thermal power units in tPower at any given moment; Energy storage charging and discharging operation and maintenance costs C E The calculation formula is: (10) In the formula, γ ES This is the operating cost coefficient for energy storage devices; For local power grid i Internal energy storage device in t The charging power at any given time; For local power grid i Internal energy storage device in t Discharge power at time System curtailment costs C a The calculation formula is: (11) In the formula, γ a The system's wind and solar curtailment penalty coefficient; For local power grid i Internal wind turbine units t Predicting output at any given moment; For local power grid i Internal wind turbine units t Actual output at any given moment; For local power grid i Internal photovoltaic units in t Predicting output at any given moment; For local power grid i Internal photovoltaic units in t Actual output at any moment In one possible design, the coordinated optimization operation model further includes constraints, which include: Power balance constraints: (12) In the formula, For local power grid i Internal fused magnesium load at t Power at time Thermodynamic equilibrium constraints: (13) In the formula, For local power grid i Internal combined heat and power units t The amount of heat generated at any given moment; For local power grid iInternal gas boilers t The amount of heat generated at any given moment; For local power grid i Inner t Heat load at any time Electricity trading constraints: (14) In the formula, For local power grid i to local power grid j Purchased electricity; For local power grid j to local power grid i Purchased electricity In one possible design, the calculation process of the nonlinear energy-sharing mapping contribution factor includes: Calculate the power grid at each level i Energy provided during the cooperation process E+ i and the energy gained E-i And define the maximum energy provided. E+ max and minimum energy provided E - max serves as the maximum and minimum reference points for the nonlinear mapping, and the calculation process is as follows: (16) (17) (18) (19) In the formula, max is the maximum value function, and min is the minimum value function. P ij,t exist t Time Power Grid i Transmitted to the power grid j The power; A nonlinear energy-sharing mapping function is constructed based on an exponential function with the natural constant e as the base. This function is then used to quantify the energy contribution of each power grid entity. The bargaining power of each entity participating in the alliance is calculated based on their energy contribution and used as the contribution factor for the nonlinear energy-sharing mapping. a i The calculation process is as follows: (20) The calculation process for the demand response contribution factor includes: The demand response contribution value of each entity is calculated using the following formula. DR i,t : (twenty one) In the formula, for t Time Power Grid i The net power exchanged between the power grid and other power grids; for t Time Power Grid i The internal load can be reduced to participate in the demand response reduction of power; for t Time Power Grid i Internally fused magnesium loads participate in demand response power reduction The demand response contribution factor is a factor that quantifies the demand response contribution capacity of each entity by using a proportional coefficient based on their respective contributions. b i The calculation process is as follows: (twenty two)

[0009] In one possible design, the overall contribution rate is determined as follows: Multifactor contribution rate u i for: (twenty three) In the formula, λ 1. λ 2 represents the proportion of the electricity contribution factor and demand response factor in the negotiations, satisfying... λ 1+ λ 2 = 1; Normalizing the contribution rates of multiple factors yields the microgrid data. i Overall contribution rate ω i for: (twenty four) Overall contribution rate ω i The larger the value, the greater the overall contribution of the entity, and the greater the operating cost compensation it receives.

[0010] In one possible design, the objective function of the multi-stakeholder benefit allocation model is: (25) (26) In the formula, C 0 i as the main body i The point of breakdown in negotiations, if i For local power grids C 0 iThe operating costs of a local area network (LAN) under traditional operating methods, excluding electricity trading, are as follows: i Mainnet C 0 i The external characteristics of the local area network (LAN) under traditional operating conditions affect the operating costs of the main network on the main network, excluding electricity trading. C i as the main body i The operating cost after participating in the globally optimal operation; C trade i as the main body i Negotiated electricity usage cost n The collection representing the subject ，i , j ∈ n and j ≠ i ; Transform the objective function into a minimum value problem: (27) (28) In the formula, R trade ij,t as the main body i Optimal transaction volume in globally optimal operation Pij,t Expected electricity trading price, R trade ji,t as the main body j Optimal transaction volume in globally optimal operation Pji,t Expected electricity trading price; when R trade ij,t=R trade ji,t This indicates that both parties have reached an agreement on the unit price of the traded electricity.

[0011] In one possible design, the multi-agent interest allocation model is solved by constructing a Lagrange function, wherein the Lagrange function is: (29) In the formula, λ ij It is a Lagrange multiplier; ρ i This is a penalty factor.

[0012] Each entity calculates its own electricity trading strategy, exchanges expected price information with each other, and updates the iteration using the following formula in each iteration. R trade ij,t ( k +1) R trade ji,t (k +1): (30) (31) (32) In the formula, k This represents the number of iterations. L i as the main body i The augmented Lagrange function, L j as the main body j The augmented Lagrangian function; λ ij ( k ) is the first k In the next iteration, with the main body i and j The Lagrange multipliers between; λ ij ( k +1) is the first k+1 In the next iteration, with the main body i and j The Lagrange multipliers between; λ ji ( k ) for the first k In the next iteration, with the main body j and i The Lagrange multipliers between; R trade ij,t ( k +1) is the first k+1 In the next iteration, the main body i The proposed hopes and main body j The unit price for trading electricity; R trade ji,t ( k +1) is the first k+1 In the next iteration, the main body j The proposed hopes and main body i The unit price for trading electricity; The convergence status can be determined using the following formula: (33) in δ This represents the upper limit of convergence; if the above formula is satisfied, the algorithm has converged. like k > k max This indicates that the algorithm does not converge. k maxThis is the maximum number of iterations set to limit the iteration time.

[0013] Secondly, this application provides a device for globally optimal operation of a local area network and a main network based on asymmetric Nash negotiation, the device comprising: The interaction characteristic analysis module is configured to determine the type of local power grid based on its functional characteristics and structural features, and to analyze the interaction characteristics of different types of local power grids under various operating modes; wherein, the types of local power grids include residential areas, high-energy-consuming industrial areas, and commercial areas; The operation model construction module is configured to construct a coordinated optimization operation model between the local power grid and the main grid. The coordinated optimization operation model aims to optimize the overall system operating cost, and the power transactions between local power grids and between the local power grid and the main grid are based on the interaction cost coefficient. The scheme determination module is configured to calculate the optimal interactive power between local power grids and between the local power grid and the main grid based on the coordinated optimization operation model, and determine the globally optimal operation scheme. The benefit allocation module is configured to construct a multi-stakeholder benefit allocation model based on asymmetric Nash negotiation theory. The multi-stakeholders include local power grids and the main grid. The multi-stakeholder benefit allocation model allocates benefits by quantifying the bargaining power of each stakeholder through the introduction of a comprehensive contribution rate. The comprehensive contribution rate is determined based on a nonlinear energy sharing mapping contribution factor and a demand response contribution factor. The operation strategy adjustment module is configured to compensate the operating costs of each entity based on the multi-entity benefit allocation model and the global optimal operation scheme, and adjust the operation strategies of the local power grid and the main grid.

[0014] Thirdly, embodiments of this application provide an electronic device, including: at least one processor and a memory; the memory stores computer execution instructions; the at least one processor executes the computer execution instructions stored in the memory, causing the at least one processor to execute the global optimal operation method for local area networks and main networks based on asymmetric Nash negotiation as described in the first aspect and various possible designs of the first aspect.

[0015] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions. When a processor executes the computer-executable instructions, it implements the global optimal operation method for local area networks and main networks based on asymmetric Nash negotiation as described in the first aspect and various possible designs of the first aspect.

[0016] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the global optimal operation method for local area networks and main networks based on asymmetric Nash negotiation as described in the first aspect and various possible designs of the first aspect.

[0017] The method, apparatus, equipment, and storage medium for globally optimal operation of local area networks and main networks based on asymmetric Nash negotiation provided in this application have at least the following beneficial effects: To address the problems of inefficiency and heavy regulation burden caused by the traditional operation mode of local area networks, this application utilizes the complementary characteristics and load demand response characteristics between different power grids. Based on the asymmetric Nash negotiation method, it establishes a coordinated and optimized scheduling model between the local power grid and the large power grid that fully considers the contribution capabilities of each entity. This reduces the overall operating cost of the system and improves the flexibility and initiative of system regulation. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] Figure 1 A flowchart of a method for global optimal operation of a local area network and a main network based on asymmetric Nash negotiation, provided for embodiments of this application; Figure 2 This is a schematic diagram illustrating the traditional operation mode and the globally optimal scheduling mode provided in the embodiments of this application; Figure 3 A diagram illustrating the coordinated and optimized operation architecture of local area networks and large power grids provided in this application embodiment; Figure 4 A flowchart illustrating the solution process for the optimal coordination model between multiple local area networks and the main network provided in this application embodiment; Figure 5 A schematic diagram of the computational system provided in the embodiments of this application; Figure 6 Clustering results of local power grids 1 and 2 and the main power grid on typical summer days provided in this application embodiment; Figure 7 A diagram showing the optimized scheduling results of local power grid 1 provided in this application embodiment; Figure 8 A diagram showing the power optimization scheduling results of the local power grid 2 provided in this application embodiment; Figure 9 A diagram showing the power optimization scheduling results of the main grid provided in this application embodiment; Figure 10 The following diagrams illustrate the optimized scheduling results for local power grids 1 and 2, and the main grid during the transition season, provided in this application embodiment. Figure 11The following diagrams illustrate the optimized scheduling results of local power grids 1 and 2 and the main power grid on a typical winter day, as provided in the embodiments of this application. Figure 12 The iterative convergence results of each subject are shown in the embodiments of this application; Figure 13 The transaction price diagrams for each entity provided in the embodiments of this application; Figure 14 The diagram illustrating the impact of the globally optimal operating mode on system flexibility, as provided in the embodiments of this application; Figure 15 A comparison chart of the operating power of fused magnesium under different operating modes of the system provided in this application embodiment; Figure 16 The diagram illustrates the impact of the fused magnesium demand response model provided in this application embodiment on system flexibility. Figure 17 A comparison chart of the corresponding fused magnesium load power considering the requirements of the embodiments of this application is provided. Figure 18 The diagram shows the structure of the local area network and main network globally optimal operation device based on asymmetric Nash negotiation provided in the embodiments of this application.

[0020] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0021] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0022] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0023] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0024] The technical solution of this application and how the technical solution of this application solves the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will now be described with reference to the accompanying drawings.

[0025] This application provides a method for globally optimal operation of a local area network and a main network based on asymmetric Nash negotiation, such as... Figure 1 As shown, the global optimal operation method for local area networks and main networks based on asymmetric Nash negotiation includes the following steps S10 to S50.

[0026] S10: Based on the functional characteristics and structural features of the local power grid, determine the type of local power grid and analyze the interaction characteristics of different types of local power grids under various operating modes; among them, the types of local power grids include residential areas, high-energy-consuming industrial areas and commercial areas.

[0027] Traditional local power grids generate and consume electricity according to their own needs, purchasing power from the main grid when necessary. This operating mode only considers the local grid's own operational needs and costs, without taking into account its relationship with the main grid's flexibility or the impact on the main grid. In contrast, under a globally optimal operating mode, each local power grid needs to comprehensively consider the flexibility matching relationship between them, seeking to maximize the overall social benefit, and utilizing the mutual assistance and support of their respective flexibility to reduce the pressure on the main grid's operation.

[0028] The scheduling diagrams for microgrids and main network under different operating modes are as follows: Figure 2 and Figure 3 As shown, local power grid 1 is a residential area, and local power grid 2 is an industrial area.

[0029] Depend on Figure 2 and Figure 3It can be seen that in period I, under the traditional operation mode, local area networks (LANs) utilize their internal renewable resources and energy storage devices, purchasing power from the main grid when power is insufficient; no LAN is willing to absorb the wind curtailment from the main grid. However, under the globally optimal operation mode, each LAN actively undertakes the task of absorbing the wind curtailment from the main grid and uses energy storage to store a portion of electricity in advance for peak load periods. In period II, under the traditional operation mode, LAN 1 has sufficient power supply and does not need to purchase power from other grids, nor is it willing to help LAN 2 absorb solar curtailment; therefore, LAN 2 experiences significant solar curtailment. Under the globally optimal operation mode, LAN 1 can reduce some of its energy storage output to help LAN 2 absorb solar curtailment. In period III, under the traditional operation mode, each LAN experiences its own peak load, purchasing power from the main grid according to its self-balancing power deficit. At this time, the main grid load is also at its peak, placing a burden on the main grid's operational safety. Under the globally optimal operating mode, LAN1 will exchange the extra electricity purchased from the main grid to LAN2, and LAN2 will release the previously reduced energy storage output during this period to cope with the peak load and reduce the amount of electricity purchased from the main grid during the peak load period.

[0030] It can be seen that under the traditional operating mode, each local power grid only considers utilizing its own distributed power sources, demand response, and energy storage devices, purchasing electricity from the main grid as needed to meet its own needs and achieve optimal economic efficiency. This operating mode does not fully utilize the flexibility and complementarity between power grids, resulting in many periods of insufficient flexibility for each grid and significant curtailment of wind and solar power. Furthermore, each local power grid must purchase electricity from the main grid during periods of power shortage, without considering whether the main grid has the flexibility to adjust power supply during those periods. This operating mode severely impacts the operational stability and economic efficiency of the main grid itself.

[0031] It can be seen that under the globally optimal operating mode, the local area network (LAN) utilizes its own energy storage devices to purchase additional electricity during periods of wind power surplus in the main grid, and then releases it during subsequent peak load periods, trading it with other power-deficient grids. This reduces their electricity purchases from the main grid and alleviates the peak-shaving pressure on the main grid. The LAN does not only consider its own interests but also aims to minimize the overall system's operating costs. Flexible complementarity can be achieved between the LAN and the main grid, and between different LANs, improving the system's overall capacity to absorb renewable resources and its operational stability.

[0032] S20: Construct a coordinated and optimized operation model between the local power grid and the main grid. The coordinated and optimized operation model aims to optimize the overall operating cost of the system, and the power transactions between local power grids and between the local power grid and the main grid are based on the interaction cost coefficient.

[0033] The operational architecture of local power grids and main power grids is as follows: Figure 4As shown, each local power grid is divided into residential areas, high-energy-consuming industrial areas, and commercial areas based on its own functional and load characteristics. Power can be exchanged between local power grids to achieve mutual flexibility and complementarity. Each local power grid can also purchase power from the main grid in a timely manner, while meeting its own flexibility requirements, through the regulation of its own flexible loads, energy storage, and high-energy-consuming loads. This achieves flexible timing matching between local power grids and between local power grids and the main grid, thereby achieving global system optimization.

[0034] This embodiment takes a residential area and a high-energy-consuming industrial area as examples. The residential area is centered around wind turbines (WT), combined heat and power (CHP), gas boilers (GB), flexible electric heat loads, and energy storage (ES). The high-energy-consuming industrial area is centered around photovoltaics (PV), adjustable fused magnesium site (FM), flexible electrical loads, and energy storage.

[0035] The coordinated optimization operation model of the local power grid and the main grid includes a cost model and constraints.

[0036] For the cost model, the operating costs of each entity in the local power grid and the large power grid are calculated using the following formula. C i : (1) In the formula, i Representing different subjects ( i =1 represents a residential area. i =2 represents a high-energy-consuming industrial zone. i =3 represents the main network. If this cost is not included in the main network, then this item is zero. The formulas for calculating each cost are as follows: 1) Local area network interconnection cost C lan For power exchange between local power grids with different objectives, the power purchaser needs to pay a reciprocal cost to the power transmission provider. (2) In the formula, γ e This represents the cost coefficient for energy exchange between local power grids. In the formula, P e ij,t For local power grid i exist t Constantly interacting with the local power grid j The amount of electricity, Pe ij,t For local power grid j exist t Constantly interacting with the local power grid i The amount of electricity, The time interval step size, T This represents the total runtime.

[0037] 2) Cost of purchasing electricity from the main power grid for the local area network C buy The costs that each local area network needs to pay when purchasing electricity from the main power grid: (3) In the formula, γ buy ( t )for t The electricity price that the local area network purchases from the main grid during certain time periods. P buy i,t For local power grid i exist t Electricity purchased from the main power grid at all times.

[0038] 3) Revenue from electricity sales from the main power grid to the local area network C 3 sell Revenue generated by a large power grid selling electricity to a local power grid: (4) (5) In the formula, the electricity sales revenue of the large power grid is numerically equal to the electricity purchase cost of the local power grid.

[0039] 4) Gas purchase cost C gas The cost of purchasing gas from an external gas network for the system: (6) In the formula, γ gas ( t )for t Gas price during specific time periods; For local power grid i Internal combined heat and power units t Gas consumption at any given moment; For local power grid i Internal gas boilers t Gas consumption at any time 5) Cost of transferable load C tran System compensation to transferable loads: (7) In the formula, γ tran This is the compensation coefficient for the system's transferable load; For local power grid i Internal transferable load in t The power of time 6) Reduced load costs C cut System compensation for load reduction: (8) In the formula, γ cut The compensation factor for load reduction in the system; For local power grid i Internal load reduction t Power at time 7) Cost of thermal power units C g System thermal power unit operating costs: (9) In the formula, γ g This is the operating cost coefficient for thermal power units; For local power grid i Internal thermal power units in t Power at any given moment.

[0040] 8) Energy storage charging and discharging operation and maintenance costs C E (10) In the formula, γ ES This is the operating cost coefficient for energy storage devices; For local power grid i Internal energy storage device in t The charging power at any given time; For local power grid i Internal energy storage device in t Discharge power at time 9) Costs of wind and solar power curtailment C a (11) In the formula, γ a The system's wind and solar curtailment penalty coefficient; For local power grid i Internal wind turbine units t Predicting output at any given moment; For local power grid i Internal wind turbine units t Actual output at any given moment; For local power grid i Internal photovoltaic units in t Predicting output at any given moment; For local power grid i Internal photovoltaic units in t Actual output at any moment The constraints include: 1) Power balance constraints (12) In the formula, For local power grid i Internal fused magnesium load at t Power at time If the power grid i If a certain item does not exist, its value is zero.

[0041] 2) Thermodynamic equilibrium constraints (13) In the formula, For local power grid i Internal combined heat and power units t The amount of heat generated at any given moment; For local power grid i Internal gas boilers t The amount of heat generated at any given moment; For local power grid i Inner t Heat load at any time Since only local power grid 1 has a thermal system in this embodiment, i Take 1.

[0042] 3) Electricity trading constraints Local power grids and large power grids with different functional objectives can achieve flexibility through the interaction of their respective electrical energy sources due to the complementary nature of their internal renewable energy sources and differences in their internal structures.

[23] Timing complementarity is implemented. The electricity trading constraints are as follows: (14) In the formula, For local power grid i to local power grid j Purchased electricity; For local power gridj to local power grid i Purchased electricity S30: Based on the coordinated optimization operation model, the optimal interactive power between local power grids and between local power grids and the main grid is calculated to determine the globally optimal operation scheme.

[0043] In this embodiment, when calculating the optimal interactive power, the measured wind power, photovoltaic and load data, as well as the corresponding thermal power unit data, can be input into the coordinated optimization operation model for calculation.

[0044] S40: A multi-stakeholder interest allocation model is constructed based on asymmetric Nash negotiation theory. The multi-stakeholders include local power grids and the main grid. The multi-stakeholder interest allocation model allocates interests by introducing a comprehensive contribution rate to quantify the bargaining power of each stakeholder. The comprehensive contribution rate is determined based on the nonlinear energy sharing mapping contribution factor and the demand response contribution factor.

[0045] The multi-agent benefit distribution model constructed in this embodiment is a Nash negotiation model, specifically a cooperative game model. After the local area network and the main network achieve global benefit optimization, multiple agents negotiate and distribute benefits according to their comprehensive contribution rate. The above problem can be divided into two sub-problems: the global optimization sub-problem (P1) and the benefit distribution sub-problem based on the comprehensive contribution rate (P2), which are solved sequentially.

[0046] Solve the globally optimal subproblem (P1).

[0047] This embodiment constructs an optimal scheduling model for local and main power grids based on the operating costs of different types of local power grids and the main grid, aiming at global optimization. The overall objective function is to optimize the operating cost of the entire system. Transaction costs between entities are no longer based on traditional electricity prices, but on electricity trading coefficients. This shifts the focus of entities from minimizing their own interests to the economic benefits gained from electricity trading. Instead, the focus is on the overall safety and economy of the system, improving its flexibility and reducing its overall operating costs. C total : (15) By solving subproblem 1, the transaction volume between the entities is calculated, providing data support for solving subproblem 2.

[0048] Solve the subproblem (P2) of benefit distribution based on the overall contribution rate.

[0049] To rationally allocate the benefits of each entity's participation in the global optimization, this embodiment uses a multi-factor comprehensive contribution rate to judge the bargaining power of each entity, including a nonlinear energy sharing mapping contribution factor and a demand response contribution factor, which respectively symbolize the magnitude of each entity's energy contribution and demand response contribution.

[0050] For the contribution factor of nonlinear energy sharing mapping, this embodiment uses the nonlinear energy sharing mapping method to quantify the contribution of different subjects to electrical energy.

[0051] Calculate the power grid at each level i Energy provided during the cooperation process E+ i and the energy gained E-i And define the maximum energy provided. E+ max and minimum energy provided E - max serves as the maximum and minimum reference points for the nonlinear mapping: (16) (17) (18) (19) In the formula, max is the maximum value function, and min is the minimum value function. P ij,t exist t Time Power Grid i Transmitted to the power grid j The power.

[0052] A nonlinear energy-sharing mapping function is constructed based on an exponential function with the natural constant e as the base. This function is then used to quantify the energy contribution of each power grid entity. The bargaining power of each entity participating in the alliance is calculated based on their energy contribution and used as the contribution factor for the nonlinear energy-sharing mapping. a i .

[0053] (20) The main significance of the nonlinear shared mapping function constructed based on the exponential function is as follows: 1) Any entity that contributes to electricity trading will receive compensation; 2) Entities that do not contribute electricity will not receive compensation; 3) The greater the contribution to electricity trading among entities, the better. a i The larger.

[0054] Regarding demand response contribution factors, each local area network (LAN) and the main network, as different stakeholders, have varying demands response contributions due to their different internal structures. Demand response necessitates adjusting one's own load to address insufficient system flexibility, which often negatively impacts the operation of one's own load. This is especially true when high-energy-consuming loads participate in system scheduling, significantly affecting the work efficiency and production processes of high-energy-consuming enterprises, and even damaging their profits. Therefore, it is necessary to offer preferential electricity prices to high-energy-consuming industrial parks as compensation for participating in system flexibility scheduling, thereby reducing their operating costs and enhancing the overall optimal willingness of all stakeholders.

[0055] Demand response contribution value of each entity DR i,t : (twenty one) In the formula, for t Time Power Grid i The net power exchanged between the power grid and other power grids; for t Time Power Grid i The internal load can be reduced to participate in the demand response reduction of power; for t Time Power Grid i Internally fused magnesium loads participate in demand response power reduction The demand response contribution factor is a factor that quantifies the demand response contribution capacity of each entity by using a proportional coefficient based on their respective contributions. b i .

[0056] (twenty two) The main significance of quantifying the demand response contribution capacity of each entity based on the proportional coefficient of each entity's demand response contribution is: 1) Any entity that contributes to the demand response will receive compensation; 2) The greater the demand response contribution of each entity, the better. b i The larger.

[0057] In multi-entity cooperation, the comprehensive contribution rate of multiple factors is a crucial factor determining the final revenue distribution coefficient of each entity. This embodiment quantifies this indicator from two perspectives: the contribution of electricity and the contribution of demand response. Each entity... i Multifactor contribution rate u i for: (twenty three) In the formula, λ 1. λ2 represents the proportion of the electricity contribution factor and demand response factor in the negotiations, satisfying... λ 1+ λ 2 = 1.

[0058] Normalizing the contribution rates of multiple factors yields the microgrid data. i Ultimate overall contribution capability or overall contribution rate ω i for: (twenty four) Overall contribution rate ω i The larger the value, the greater the overall contribution of the entity, and the greater the operating cost compensation it receives.

[0059] In this embodiment, in the benefit distribution model based on asymmetric Nash negotiation, the local area network (LAN) and the main network, as different entities, have different energy contributions during the globally optimal operation. Those with larger contributions have higher power in the negotiation process, allowing them to obtain greater benefits in the globally optimal operation. Generally, the transaction price between them must be lower than the electricity price purchased under the traditional operation mode; that is, the transaction price after negotiation must be lower than the transaction price before negotiation, otherwise negotiation cannot proceed. The objective function of this benefit distribution model is expressed as: (25) (26) In the formula, C 0 i as the main body i The point of breakdown in negotiations, if i For local power grids C 0 i The operating costs of a local area network (LAN) under traditional operating methods, excluding electricity trading, are as follows: i Mainnet C 0 i The external characteristics of the local area network (LAN) under traditional operating conditions affect the operating costs of the main network on the main network, excluding electricity trading. C i as the main body i The operating cost after participating in the globally optimal operation; C trade i as the main body i Negotiated electricity usage cost n The collection representing the subject ， That is i , j ∈ n and j ≠ i .

[0060] The participants in the Nash negotiations aim to maximize the gains from participating in the globally optimal operation compared to operating alone. Inequality (25) restricts the setting of the electricity trading price to ensure that all participants benefit, thus satisfying all parties in the negotiation. Taking the logarithm of equation (26) transforms a maximum value problem into a minimum value problem for easier solution: (27) (28) In the formula, R trade ij,t as the main body i Optimal transaction volume in globally optimal operation Pij,t Expected electricity trading price, R trade ji,t as the main body j Optimal transaction volume in globally optimal operation Pji,t Expected electricity trading price; when R trade ij,t=R trade ji,t This indicates that both parties have reached an agreement on the unit price of the traded electricity.

[0061] The specific Lagrange function is as follows: (29) In the formula, λ ij It is a Lagrange multiplier; ρ i This is a penalty factor.

[0062] Each entity needs to calculate its own electricity trading strategy and exchange expected price information with each other. Each iteration requires updating the iteration information. R trade ij,t ( k +1) R trade ji,t ( k +1): (30) (31) In the formula, k This represents the number of iterations. L i as the main body i The augmented Lagrange function, L j as the main body j The augmented Lagrangian function; λ ij ( k ) is the firstk In the next iteration, with the main body i and j The Lagrange multipliers between; λ ij ( k +1) is the first k+1 In the next iteration, with the main body i and j The Lagrange multipliers between; λ ji ( k ) for the first k In the next iteration, with the main body j and i The Lagrange multipliers between; R trade ij,t ( k +1) is the first k+1 In the next iteration, the main body i The proposed hopes and main body j The unit price for trading electricity; R trade ji,t ( k +1) is the first k+1 In the next iteration, the main body j The proposed hopes and main body i The unit price for trading electricity.

[0063] (32) Determining convergence: (33) in δ This indicates that the upper bound of convergence is 10. -3 If the above equation is satisfied, it means that the algorithm has converged.

[0064] like k > k max This indicates that the algorithm does not converge. k max This is the maximum number of iterations set to limit the iteration time.

[0065] S50: Based on the multi-entity benefit distribution model and the global optimal operation plan, compensate for the operating costs of each entity and adjust the operation strategies of the local power grid and the main grid.

[0066] Through steps S10-S50 above, this embodiment constructs an optimal coordination model between multiple local area networks and the main network, including the coordination optimization operation model and multi-stakeholder benefit allocation model constructed in steps S20 and S40. The first stage adopts the operation scheme with the optimal overall system operating cost; the second stage compensates for operating costs according to the contribution of each stakeholder. The specific flowchart is as follows... Figure 5 As shown: The first step is to input the measured wind power, photovoltaic, and load data of a province in Northwest China, and then provide the corresponding thermal power unit data.

[0067] The second step is to cluster the raw data to identify different typical days based on different seasons.

[0068] The third step is to calculate the interactive electricity volume between the two local power grids and between the local power grid and the main grid. The transaction decision aims for global optimization, meaning that each transaction must reduce overall operating costs.

[0069] The fourth step is to calculate the system operating cost for each transaction scenario, including the operating costs of each local area network and the main network. If the operating cost is minimized, it is the globally optimal operating scheme, and the transaction electricity is output. If the operating cost is not minimized, the scheme is not optimal, and the transaction electricity is updated again until the optimal scheme is found.

[0070] The fifth step is to enter the operating cost compensation stage. Input the globally optimal interaction power obtained in the first stage.

[0071] The sixth step is to calculate the bargaining factor of each entity based on its economic and operational contributions to the overall optimal outcome.

[0072] Step 7: Perform error judgment. If the error requirement is met, compensate each entity for operating costs according to the negotiation result, and output the optimal negotiation result and the operating cost of each entity. If the convergence standard is not reached after exceeding the number of iterations, output "Algorithm error".

[0073] To further illustrate the feasibility and progressiveness of the method proposed in this application, a detailed explanation will be provided below with specific calculation examples.

[0074] This example uses two local power grids and one large power grid to discuss the coordination optimization problem, such as... Figure 6As shown in the table. Local power grid 1 is a residential area with electricity and heat load demands. Its power source mainly consists of wind power and CHP within the local power grid, while its heat source consists of CHP and GB. Local power grid 2 is a high-energy-consuming industrial area with not only ordinary electricity loads but also fused magnesium loads with adjustable power participating in system dispatch. Its power source consists of photovoltaic and thermal power plants within its local grid. The main power grid consists of its electricity loads, thermal power units, and wind power. Each part of the grid has its own energy storage device. The electricity purchase price for the local power grid is shown in Table 1, the gas purchase price is shown in Table 2, and other system operating parameters and cost coefficients are shown in Tables 3 and 4. Typical daily data for renewable energy and loads of local power grids and the main grid with different functional purposes are shown in the appendix. Figure 7 As shown.

[0075] Table 1 Local Area Network Electricity Purchase Price List

[0076] Table 2 Gas Purchase Price List

[0077] Table 3 System Operating Parameters

[0078] Table 4 System Operating Cost Coefficients

[0079] The system modeling process is as follows: 1) Energy storage system model.

[0080] power grid i ES in t State of charge at time: (B1) In the formula, η cES η dES represents the charge / discharge efficiency of the energy storage device.

[0081] Charge and discharge power constraints: (B2) (B3) In the formula, P Ec i, max、 P Ed i,max respectively power grid i The upper limit of the charging and discharging power of the energy storage device.

[0082] Energy storage capacity constraints: (B4) In the formula, Soc i, max、 Soc i, min power grid i The upper and lower limits of the power capacity of the energy storage device.

[0083] 2) Combined heat and power unit model.

[0084] Local power grid i CHP in t Electricity output at any moment P CHP i,t Its natural gas consumption V CHP i,t The relationship is as follows: [B1,B2] : (B5) In the formula, η CHP ( ke CHP ,t The parameter represents the thermoelectric conversion efficiency of CHP in LAN 1, and is also the electrical load factor of the CHP equipment. ke CHP ,t The function, Q CH 4 represents the calorific value of natural gas, which is taken as 9.7 kW·h / m³ in this embodiment. 3[B3] Considering its characteristic of "heat determines electricity", its thermoelectric conversion relationship is as follows: (B6) In the formula, H CHP i,t For local power grid 1 ( i =1) in CHP t Constant heat output.

[0085] 3) Gas-fired boiler model.

[0086] In this embodiment, the gas-fired boiler serves as an additional heat source besides CHP to meet the heat load requirements. Its output characteristics are as follows: (B7) In the formula, H GB i,t For local power grid i GB in t Constant heat output V GB i,t For local power grid 1 ( i =1) GB in t Natural gas consumption at any given time. η GB ( ke GB ,t The thermoelectric conversion efficiency of GB in LAN 1 is . ke GB ,t The load rate is in GB.

[0087] 4) Ordinary electrical and thermal load model.

[0088] Local power grids i Ordinary actual electrical load P e i,t Can be derived from the original electrical load P e0 i,t It can reduce electrical load. P cut i,t and transferable electrical loads P tran i,t composition: (B8) Reduce electrical load P cut i,t and transferable electrical loads P tran i,t Upper and lower bound constraints: (B9) (B10) (B11) In the formula, k ecut i The reduction factor is the load reduction factor that can be reduced. k etran i The transferability factor is the transferability coefficient of the transferable load.

[0089] Since the heat load also has a certain degree of adjustability, local area network 1 ( i The heat load of =1) can be derived from the original heat load. P h0 i,t and can reduce heat load P DR i,t constitute: (B12) Reduce heat load P DR i,t It needs to satisfy its upper and lower bound constraints: (B13) In the formula, k hcut i This is the reduction factor for the load that can be reduced.

[0090] 5) High energy load model.

[0091] Fused magnesium load is a high-energy-consuming load. Due to its generally large power, it can participate in the flexible adjustment of the system when used properly. However, to avoid safety accidents and product quality problems, the fused magnesium load needs to be adjusted within a reasonable range. (B14) (B15) In the formula, P base i,t This is the base load for fused magnesium load to operate normally. P up i,t and P down i,t For example, electric fused magnesium furnaces at t The operating power after the adjustment and the operating power after the adjustment are both adjusted. S base t , S up t and S down t These are the normal operating state variables, the upward adjustment operating state variables, and the downward adjustment operating state variables for fused magnesium, respectively. S base t =1 indicates an electric fused magnesium furnace t Always operating at normal power. S up t =1 indicates an electric fused magnesium furnace t Always operate at the increased power (high power). S down t =1 indicates an electric fused magnesium furnace t Always operate at the reduced power (low power).

[0092] Because fused magnesium enterprises involve issues such as product output, quality, and employee shift work hours, constraints are imposed on the adjustment power, time, and frequency of fused magnesium adjustments. [B4] : (B16) (B17) (B18) In the formula, T base , T up and T down These represent the shortest switching times for the three operating states of the electric fused magnesium furnace. Δ P Mg i,t for t The change in fused magnesium load at a given time compared to the previous time.S Mg t With Δ P Mg i,t The relevant variables, when Δ P Mg i,t When it is 0, S Mg t When Δ is 1 P Mg i,t When it is not 0, S Mg t It is 0. M Mg This represents the maximum number of times the fused magnesium load can be adjusted.

[0093] This embodiment is compiled using MATLAB R2019a and solved using the solvers Gurobi and mosek.

[0094] To verify the effectiveness of the coordinated optimization scheduling model for different types of local area networks and large power grids proposed in this application with the goal of global optimization, and considering the flexibility timing matching relationship between local area networks and large power grids, this embodiment sets up four schemes for comparison, as shown in Table 5.

[0095] Table 5. Case Settings

[0096] Analysis of optimization results for sub-problem 1.

[0097] This embodiment clusters annual wind and solar power data from a region in Northwest China according to seasons, obtaining typical daily data for the spring-autumn transition season, summer, and winter. Then, it compares and analyzes each typical day scenario to demonstrate the economic efficiency and effectiveness of the proposed method. Due to space limitations, this embodiment focuses on a typical summer day, with the dispatching results of various levels of the power grid as follows: Figures 7 to 9 As shown. Other typical daily scheduling results are shown below. Figure 10 , 11 The operating costs of each scheme for typical summer day data are shown in Table 6.

[0098] Table 6 Comparison of Operating Costs of Each Solution

[0099] Depend on Figures 7 to 9 The dispatch results show that the optimal coordinated operation mode between the local area network and the main power grid ensures full utilization of renewable resources in power grids at all levels, resulting in less wind and solar power curtailment. The complementary nature of renewable resources within each power grid ensures the diversity and reliability of its power supply sources, thereby improving the flexibility of each power grid.

[0100] Depend on Figure 7As can be seen from (a) and (b), during periods 1-6, 13-16, and 22-24 when its own load is relatively low and the main grid has abundant renewable resources, local grid 1 obtains more electricity from the main grid to meet its own load demand during these periods and to meet future peak load demand. It also utilizes its load response to increase or decrease load demand and stores some of the electricity in energy storage devices, thus achieving its own flexibility needs while absorbing wind power from the main grid. During periods 7-12, residential electricity and heat loads are high, requiring output from CHP and GB, and the energy storage devices release their stored electricity to cope with the increase in electricity load. During periods 15-21, because local grid 2 cannot maintain its own power supply, it needs local grid 1 to supply some electricity. Therefore, local grid 1 releases its energy storage at this time and utilizes its internal load demand response to reduce load demand.

[0101] Depend on Figure 8 It can be seen that during periods 1-8, when the main grid has a large surplus of wind power, local grid 2 operates at maximum power due to its own high-energy-consuming load. It utilizes the high energy consumption characteristics of its own high-energy-consuming load to consume wind power that the main grid cannot absorb. Energy storage devices charge when they receive sufficient power from the main grid and discharge when they receive insufficient power, reducing wind curtailment. From periods 9-18, local grid 2 enters its peak photovoltaic generation phase, with its high-energy-consuming load continuing to operate at maximum power, and energy storage devices charging extensively. From periods 19-22, local grid power is scarce. During this period, photovoltaic output is insufficient, and the industrial park experiences peak loads other than high-energy-consuming loads. Since the main grid does not provide enough power to local grid 2, local grid 2 needs to obtain some power from local grid 1 to maintain its own load demand, and high-energy-consuming loads operate at minimum power. From periods 23-24, due to a surplus of wind power in the main grid and a decrease in local grid 2's own load, high-energy-consuming loads in local grid 2 operate at rated power to increase wind power absorption.

[0102] Depend on Figure 9 It can be seen that the main power grid has a surplus of wind power during periods 1-9 and 14-19, which can be transferred to local power grids 1 and 2 for utilization. During periods 9-13 and 20-22, the main power grid load reaches its peak, while wind power is at its lowest. Thermal power units increase their output and reduce the power transmission to local power grids 1 and 2.

[0103] As shown in Table 6, Scheme 1 proposed in this embodiment has the lowest overall operating cost compared to the other three schemes, reducing operating costs by 13.03%, 4.11%, and 14.04%, respectively. The reduction in operating costs demonstrates that the globally optimal operating mode of the local area network and the main power grid (Scheme 1) significantly improves operational economy compared to the traditional operating mode where the local area network operates independently on the main power grid (Scheme 2). The use of the fused magnesium load demand response model (Scheme 1) also shows improved economic efficiency compared to not using the fused magnesium load demand response model (Scheme 3).

[0104] Analysis of the iteration results of subproblem 2.

[0105] This embodiment uses the ADMM algorithm for the second stage of distributed solution. The convergence status of each main body iteration is shown in [link to documentation]. Figure 12 As shown.

[0106] Depend on Figure 12 It can be seen that the negotiation among the parties converged to within the error requirement range on the 21st iteration, with a computation time of 20.46 seconds. This verifies that the method proposed in this application has good computational speed and convergence performance.

[0107] The electricity exchange price between various entities, such as Figure 13 As shown.

[0108] Depend on Figure 13 It is known that the asymmetric price negotiation among the various entities must be lower than the unit price of electricity under the traditional operation mode in each time period.

[0109] Analysis of the distribution of benefits among the various entities.

[0110] The distribution of benefits in the asymmetric Nash negotiations among the stakeholders is shown in Table 7. In the traditional operation mode, the local power grid purchases electricity from the main grid according to its own electricity demand, with the purchase price based on the standard price. Under the globally optimal operation mode (before negotiation), the operation mode of sub-problem 1 is adopted. Under the globally optimal operation mode (after negotiation), the transaction price for each stakeholder adopts the asymmetric Nash game price proposed in this section.

[0111] Table 7 Comparison of Operating Costs of Each Scheme

[0112] As shown in Table 7, the operating costs of each entity after the negotiation in this embodiment are lower than the operating costs of each entity using the traditional operating methods. The operating costs of each entity were reduced by 17.75%, 20.88%, and 27.75%, respectively, which fully mobilized the initiative of each entity.

[0113] In the globally optimal operating mode before price negotiation (where electricity trading is cost-free), the operating cost of LAN 2 is lower. This is because, under the globally optimal operating mode, LAN 2 receives free energy exchange from other entities, reducing its own curtailment of solar power and reducing scheduling compensation for high-energy-consuming loads. Compared to the globally optimal operating mode before price negotiation (where electricity trading is cost-free), this embodiment comprehensively considers the contributions of each entity to electricity consumption and demand response, correspondingly reducing the operating costs of LAN 1 and the main grid, while increasing the operating cost of LAN 2.

[0114] Before price negotiation (where electricity trading incurs costs), the main grid's operating costs are lower in the globally optimal operating mode because it significantly reduces its own operating costs by selling its own electricity. However, LAN 2's operating costs increase because it purchases large amounts of electricity from other entities to operate under high energy loads, thus increasing its own operating costs. Compared to the globally optimal operating mode before price negotiation (where electricity trading incurs costs), this embodiment comprehensively considers the contributions of each entity to electricity consumption and demand response, correspondingly reducing the operating costs of LAN 1 and LAN 2, while increasing the main grid's operating costs.

[0115] In summary, the asymmetric Nash negotiation model proposed in this embodiment comprehensively considers the contributions of each entity to electricity consumption and demand response. Whether compared to before the negotiation (when electricity trading is costless) or before the negotiation (when electricity trading has costs), it yields a more reasonable distribution of benefits. This ensures that entities like the main grid, which contribute significantly to electricity consumption in the game, receive corresponding operating cost compensation, and entities like LAN2, which contribute significantly to demand response, also receive corresponding operating cost compensation. This results in better motivation and fairness among the various entities in the game during the interaction process.

[0116] Analysis of the impact of the globally optimal operating mode on system flexibility.

[0117] To further analyze the impact of optimal and conventional operating modes on system flexibility in local and large power grids, system flexibility analyses are conducted for Scheme 1 and Scheme 2 respectively. The flexibility indices of each scheme are compared below. Figure 14 As shown.

[0118] from Figure 14 As can be seen from (a), Scheme 1 results in less output from the main grid's thermal power units compared to Scheme 2. This is because the globally optimal operating mode allows for flexible complementarity between local power grids, reducing the demand on the main grid's thermal power. From... Figure 14 As can be seen from (b), Scheme 1 significantly improves the wind curtailment situation compared to Scheme 2. During periods of abundant wind power, the main grid transmits a large amount of wind power to various power-deficient local area networks, reducing its own wind curtailment costs. From Figure 14As can be seen from (c), Scheme 1 has a smaller peak-to-valley difference in net load compared to Scheme 2, and the smaller fluctuation in net load is beneficial to the regulation of thermal power units in the main grid. From Figure 14 As can be seen from (d), although Scheme 1 has less flexibility in adjusting its power output compared to Scheme 2 during periods of high wind power generation, Scheme 1 still has sufficient flexibility. However, during critical periods when the main grid's adjustment flexibility is insufficient (19:00-22:00), Scheme 1 still has sufficient flexibility, while Scheme 2's flexibility is severely insufficient. That is, Scheme 1, which adopts the globally optimal operating mode, consistently has sufficient flexibility, while Scheme 2's flexibility is significantly insufficient during certain periods. Figure 14 As can be seen from (e), for the vast majority of periods, Option 1 has greater flexibility in adjusting its power output than Option 2. Only during the period from 17:00 to 20:00 does Option 1 have slightly less flexibility in adjusting its power output than Option 2, because Option 2 has a larger thermal power output than Option 1, thus allowing for some room for adjustment.

[0119] Depend on Figure 15 It can be seen that Scheme 1, which adopts the globally optimal operation mode, actively increases the operating power of the fused magnesium furnace during the 1-8 period when the main grid has surplus wind power and low load, thus actively participating in the main grid's wind power consumption and reducing the system's wind curtailment. In contrast, Scheme 2, which adopts the traditional operation mode, operates at conventional power during the 1-8 period due to insufficient photovoltaic power of its own capacity, in order to reduce the amount of electricity purchased from the main grid and thus reduce its own operating costs, without absorbing more of the main grid's wind curtailment.

[0120] comprehensive Figure 14 and Figure 15 It can be seen that the globally optimal operating mode proposed in this embodiment can make the main grid more flexible, have more ideal upward and downward adjustment space, absorb more renewable energy, and make the operating power of fused magnesium more reasonable, thereby reducing the system operating cost.

[0121] Analysis of the impact of regulation characteristics of high-energy-consuming industrial local power grids on system flexibility To further analyze the impact of the demand response model for fused magnesium on system flexibility, system flexibility analyses were conducted for Scheme 1 and Scheme 3 respectively. The flexibility indices of each scheme are compared below. Figure 16 As shown. The operating power of fused magnesium in Schemes 1 and 3 is as follows. Figure 17 As shown.

[0122] from Figure 16 As can be seen from (a), Scheme 1 results in lower output from the main grid's thermal power units compared to Scheme 3. This is because the fused magnesium load in local grid 2 reduces the operating power of the fused magnesium furnaces when the main grid's power supply is difficult, thus reducing the main grid's demand for thermal power. From Figure 16As can be seen from (b), Scheme 1 demonstrates significantly better wind power absorption compared to Scheme 3. The use of fused magnesium in system regulation greatly enhances the absorption of wind power from the main grid, thereby reducing the overall wind curtailment penalty. From... Figure 16 As can be seen from (c), Scheme 1 has a smaller net load with a smaller peak-to-valley difference than Scheme 3, indicating that the adjustment of the fused magnesium load reduces the net load fluctuation and plays a certain role in "peak shaving and valley filling". From Figure 16 As can be seen from (d), although Option 1's flexibility in adjusting upwards is lower than Option 3 in certain time periods, Option 1 still possesses sufficient flexibility in adjusting upwards during those periods. However, in the period from 20:00 to 21:00, Option 3's flexibility in adjusting upwards is not only lower than Option 1's, but its flexibility in adjusting upwards is also significantly insufficient. From... Figure 16 As can be seen from (e), for the vast majority of the time periods, Option 1 has a greater flexibility in adjusting downwards than Option 3. Only at time 21 does Option 1 have a slightly smaller flexibility in adjusting downwards than Option 3.

[0123] Depend on Figure 16 It can be seen that Scheme 1, when the system has abundant wind and solar power, increases the load power of the fused magnesium furnace to absorb the system's renewable resources and reduce the system's energy curtailment costs. Conversely, when the system has insufficient wind and solar power, the operating power of the fused magnesium furnace is reduced to alleviate the system's energy supply pressure and impact on system flexibility. Scheme 3, with high loads operating at constant power, cannot participate in the system's flexibility adjustment.

[0124] comprehensive Figure 16 and Figure 17 It can be seen that the electrofused magnesium demand response model proposed in this embodiment can make the system more flexible, absorb more renewable resources while meeting its product output and quality requirements, smooth out fluctuations in the system's net load, and reduce system operating costs.

[0125] To address the inefficiencies and heavy regulatory burdens inherent in traditional local area network (LAN) operation modes, this embodiment leverages the complementary characteristics and load demand response characteristics between different power grids. Based on the asymmetric Nash negotiation method, it establishes a coordinated and optimized scheduling model between the LAN and the main power grid, fully considering the contributions of each entity. This reduces the overall system operating cost and improves the flexibility and proactivity of system regulation. Analysis of different schemes leads to the following conclusions: 1) To address the lack of flexibility in the main grid caused by the traditional local power grid operation mode of purchasing power when there is a power shortage and sending surplus power to the grid, we analyze the advantages of various types of local power grids compared with the main grid's globally optimal operation mode.

[0126] 2) A multi-type coordinated optimization scheduling model for local power grids and the main grid, aiming at global optimization, was constructed to meet the power load of each power grid. This model ensures the output, quality, and operational characteristics of energy-intensive enterprises while improving the overall flexibility of the system and reducing the regulation burden on the main grid. Compared with the other three schemes, the proposed scheme reduces operating costs by 13.03%, 4.11%, and 14.04%, respectively, and increases the renewable energy absorption rate by 10.37%, 2.01%, and 11.46%, respectively.

[0127] 3) A bargaining model for different entities based on the comprehensive contribution rate was constructed, and bargaining factors for energy mutual assistance and demand response were designed to comprehensively evaluate the bargaining capabilities of different entities, fully mobilize the enthusiasm of each entity, and redistribute the operating costs reduced by the global optimization of the system so that each entity can receive operating cost compensation corresponding to its contribution.

[0128] This application also provides a device for globally optimal operation of a local area network and a main network based on asymmetric Nash negotiation, such as... Figure 18 As shown, the local area network and main network globally optimal operation device based on asymmetric Nash negotiation includes: The interaction characteristic analysis module 2701 is configured to determine the type of local power grid based on its functional characteristics and structural features, and to analyze the interaction characteristics of different types of local power grids under various operating modes; wherein, the types of local power grids include residential areas, high-energy-consuming industrial areas, and commercial areas. The operation model construction module 2702 is configured to construct a coordinated optimization operation model between the local power grid and the main grid. The coordinated optimization operation model aims to optimize the overall system operating cost, and the power transactions between local power grids and between the local power grid and the main grid are based on the interaction cost coefficient. The scheme determination module 2703 is configured to calculate the optimal interactive power between local power grids and between the local power grid and the main grid based on the coordinated optimization operation model, and determine the global optimal operation scheme. The benefit allocation module 2704 is configured to construct a multi-stakeholder benefit allocation model based on asymmetric Nash negotiation theory; wherein, the multi-stakeholder includes each local power grid and the main grid, and the multi-stakeholder benefit allocation model allocates benefits by quantifying the bargaining power of each stakeholder by introducing a comprehensive contribution rate, and the comprehensive contribution rate is determined based on a nonlinear energy sharing mapping contribution factor and a demand response contribution factor; The operation strategy adjustment module 2705 is configured to compensate the operating costs of each entity and adjust the operation strategies of the local power grid and the main grid according to the multi-entity benefit allocation model and the global optimal operation scheme.

[0129] This application provides an electronic device. The electronic device may include a processor and a memory, wherein the processor and the memory can communicate; exemplarily, the processor and the memory communicate via a communication bus.

[0130] The processor executes computer execution instructions stored in memory, causing the processor to perform the scheme in the above embodiments. The processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0131] The communication bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The system bus can be divided into address bus, data bus, control bus, etc. Transceivers are used to enable communication between database access devices and other computers (e.g., clients, read-write libraries, and read-only libraries). Memory may include random access memory (RAM) and may also include non-volatile memory.

[0132] The electronic device provided in this application embodiment can be the terminal device described in the above embodiments.

[0133] This application also provides a computer-readable storage medium storing computer instructions. When the computer instructions are executed on a computer, the computer performs the technical solution of the above-described embodiment of the global optimal operation method for local area networks and main networks based on asymmetric Nash negotiation.

[0134] This application also provides a computer program product, which includes a computer program stored in a computer-readable storage medium. At least one processor can read the computer program from the computer-readable storage medium. When the at least one processor executes the computer program, it can implement the technical solution of the global optimal operation method of local area network and main network based on asymmetric Nash negotiation in the above embodiments.

[0135] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be indirect coupling or communication connection through some interfaces, devices, or modules, and may be electrical, mechanical, or other forms.

[0136] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to implement the solution of this embodiment according to actual needs.

[0137] Furthermore, the functional modules in the various embodiments of this application can be integrated into one processing unit, or each module can exist physically separately, or two or more modules can be integrated into one unit. The unit composed of the above modules can be implemented in hardware or in the form of hardware plus software functional units.

[0138] The integrated modules described above, implemented as software functional modules, can be stored in a computer-readable storage medium. These software functional modules, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps of the methods of the various embodiments of this application.

[0139] It should be understood that the aforementioned processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. A general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly manifested as execution by a hardware processor, or execution by a combination of hardware and software modules within the processor.

[0140] The memory may include high-speed RAM, and may also include non-volatile storage (NVM), such as at least one disk storage device, and may also be a USB flash drive, external hard drive, read-only memory, disk or optical disc, etc.

[0141] Buses can be Industry Standard Architecture (ISA) buses, Peripheral Component Interconnect (PCI) buses, or Extended Industry Standard Architecture (EISA) buses, etc. Buses can be categorized into address buses, data buses, control buses, etc.

[0142] The aforementioned storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The storage medium can be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0143] An exemplary storage medium is coupled to a processor, enabling the processor to read information from and write information to the storage medium. Alternatively, the storage medium can be an integral part of the processor. The processor and storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and storage medium can exist as discrete components in an electronic control unit or main control device.

[0144] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for achieving globally optimal operation of a local area network and a main network based on asymmetric Nash negotiation, characterized in that, The method includes: Based on the functional characteristics and structural features of the local power grid, the types of local power grids are determined, and the interaction characteristics of different types of local power grids under various operating modes are analyzed; wherein, the types of local power grids include residential areas, high-energy-consuming industrial areas, and commercial areas; A coordinated optimization operation model for local power grids and the main grid is constructed. The coordinated optimization operation model aims to optimize the overall system operating cost, and the power transactions between local power grids and between local power grids and the main grid are based on the interaction cost coefficient. Based on the aforementioned coordinated optimization operation model, the optimal interactive power between local power grids and between local power grids and the main grid is calculated, and the globally optimal operation scheme is determined. A multi-stakeholder benefit allocation model is constructed based on asymmetric Nash negotiation theory. The multi-stakeholders include local power grids and the main grid. The multi-stakeholder benefit allocation model allocates benefits by quantifying the bargaining power of each stakeholder through the introduction of a comprehensive contribution rate. The comprehensive contribution rate is determined based on a nonlinear energy sharing mapping contribution factor and a demand response contribution factor. Based on the multi-entity benefit distribution model and the global optimal operation scheme, the operating costs of each entity are compensated, and the operation strategies of the local power grid and the main grid are adjusted.

2. The method for globally optimal operation of local area networks and main networks based on asymmetric Nash negotiation according to claim 1, characterized in that, In the coordinated optimization operation model, the operating costs C of each entity in the local power grid and the large power grid are determined by the following formula. i : In the formula, C i This represents the operating costs of different entities, where i=1 represents residential areas, i=2 represents high-energy-consuming industrial areas, and i=3 represents the main grid. If the entity does not include this cost, then this item is zero; C lan Represents the cost of local area network mutual assistance, C buy This represents the cost of purchasing electricity from the main power grid by the local area network. C represents the revenue from electricity sales from the main power grid to the local area network. gas C represents the cost of purchasing gas. tran Represents the cost of transferable loads, C cut This represents a reduction in load costs, C g C represents the cost of thermal power units. E C represents the operating and maintenance costs of energy storage charging and discharging. a Represents the cost of wind and solar power curtailment in the system; in: Local area network mutual assistance cost C lan The calculation formula is: In the formula, γ e This refers to the cost coefficient for energy sharing between local power grids. Let be the amount of electricity that local power grid i exchanges with local power grid j at time t. Let Δt be the amount of electricity that local power grid j exchanges with local power grid i at time t, where Δt is the time interval step size and T is the total running time. The cost C of purchasing electricity from the main power grid for a local area network buy The calculation formula is: In the formula, γ buy (t) represents the electricity price paid by the local area network to the main grid during time period t. Let represent the amount of electricity that local power grid i purchases from the main power grid at time t. Revenue from electricity sales from the main power grid to the local area network The calculation formula is formula (4) or formula (5): Gas purchase cost C gas The calculation formula is: In the formula, γ gas (t) represents the gas purchase price during time period t; The gas consumption of the combined heat and power unit within the local power grid i at time t; Let be the gas consumption of the gas-fired boiler within the local power grid i at time t; Transferable load cost C tran The calculation formula is: In the formula, γ tran This is the compensation coefficient for the system's transferable load; Let be the power of the transferable load within the local power grid i at time t; Reduced load costs C cut The calculation formula is: In the formula, γ cut The compensation factor for load reduction in the system; The power that can be reduced from the load at time t within the local power grid i; Cost of thermal power units C g The calculation formula is: In the formula, γ g This is the operating cost coefficient for thermal power units; Let be the power of the thermal power units within the local power grid i at time t; Energy storage charging and discharging operation and maintenance cost C E The calculation formula is: In the formula, γ ES This is the operating cost coefficient for energy storage devices; Let be the charging power of the energy storage device within the local power grid i at time t; Let be the discharge power of the energy storage device within the local power grid i at time t; System curtailment cost C a The calculation formula is: In the formula, γ a The system's wind and solar curtailment penalty coefficient; The predicted output of wind turbines within the local power grid i at time t; The actual output of the wind turbines within the local power grid i at time t; The predicted output of photovoltaic units within the local power grid i at time t; This represents the actual output of the photovoltaic units within the local power grid i at time t.

3. The method for globally optimal operation of local area networks and main networks based on asymmetric Nash negotiation according to claim 2, characterized in that, The coordinated optimization operation model also includes constraints, which include: Power balance constraints: In the formula, Let be the power of the fused magnesium load within the local power grid i at time t; Thermodynamic equilibrium constraints: In the formula, The heat output of the combined heat and power unit within the local power grid i at time t; The heat output of the gas-fired boiler within the local power grid i at time t; The magnitude of the heat load of the local power grid at time t; Electricity trading constraints: In the formula, The electrical energy purchased by local grid i from local grid j; The electrical energy purchased by local grid j from local grid i.

4. The method for globally optimal operation of local area networks and main networks based on asymmetric Nash negotiation according to claim 1, characterized in that, The calculation process of the nonlinear energy-sharing mapping contribution factor includes: Calculate the energy E provided by each level of the power grid i during the cooperation process. i + and the energy gained And define the maximum energy provided. and the minimum value of energy provided The calculation process is as follows, using the maximum and minimum reference points for the nonlinear mapping: In the formula, max is the maximum value function, min is the minimum value function, and P ij,t The power transmitted from grid i to grid j at time t; A nonlinear energy-sharing mapping function is constructed based on an exponential function with the natural constant e as the base. This function is then used to quantify the power contribution of each power grid entity. The bargaining power of each entity participating in the alliance is calculated based on their power contribution and used as the nonlinear energy-sharing mapping contribution factor a. i The calculation process is as follows: The calculation process for the demand response contribution factor includes: The demand response contribution value (DR) of each entity is calculated using the following formula. i,t : In the formula, Let be the net power exchanged between grid i and other grids at time t; Let t be the power of the grid that can be reduced to participate in demand response reduction within time t; Let be the power reduction of the fused magnesium load in grid i at time t, which is achieved through demand response. The demand response contribution factor b is a demand response contribution factor that quantifies the magnitude of each entity's demand response contribution capability by using a proportional coefficient based on each entity's demand response contribution. i The calculation process is as follows:

5. The method for globally optimal operation of local area networks and main networks based on asymmetric Nash negotiation according to claim 4, characterized in that, The overall contribution rate is determined as follows: Multifactor contribution rate u i for: u i =λ1a i +λ2b i (23) In the formula, λ1 and λ2 are the proportions of the electricity contribution factor and the demand response factor in the negotiation, satisfying λ1+λ2=1; By normalizing the contribution rates of multiple factors, the comprehensive contribution rate ω of microgrid i can be obtained. i for: Overall contribution rate ω i The larger the value, the greater the overall contribution of the entity, and the greater the operating cost compensation it receives.

6. The method for globally optimal operation of local area networks and main networks based on asymmetric Nash negotiation according to claim 5, characterized in that, The objective function of the multi-stakeholder interest distribution model is: In the formula, If the negotiation breakdown point is subject i, and i is a local power grid, then This refers to the operating costs of a local area network (LAN) under traditional operating conditions, excluding electricity trading. If i is the main network, then... C represents the operating costs of the main network on the main network, excluding electricity trading, under the traditional operating mode, due to the external characteristics of the local area network; i The operating cost of subject i after participating in the globally optimal operation; Let i be the electricity exchange cost after negotiation for subject i, and n represent the set of subjects, i, j∈n and j≠i; Transform the objective function into a minimum value problem: In the formula, Subject i operates at the optimal level globally and generates the optimal transaction volume. Expected electricity trading price, For subject j, the optimal trading volume is achieved through global optimization. Expected electricity trading price; when This indicates that both parties have reached an agreement on the unit price of the traded electricity.

7. The method for globally optimal operation of a local area network and main network based on asymmetric Nash negotiation according to claim 6, characterized in that, The multi-agent interest allocation model is solved by constructing a Lagrange function, which is: In the formula, λ ij For Lagrange multipliers; ρ i This is a penalty factor. Each entity calculates its own electricity trading strategy, exchanges expected price information with each other, and updates the iteration using the following formula in each iteration. In the formula, k is the number of iterations; L i For the augmented Lagrangian function of subject i, L j The augmented Lagrangian function of subject j; λ ij (k) represents the Lagrange multiplier between the subjects i and j at the k-th iteration; λ ij (k+1) represents the Lagrange multiplier between the subjects i and j at the (k+1)th iteration; λ ji (k) is the Lagrange multiplier between the subjects j and i at the k-th iteration; The unit price proposed by subject i for electricity trading with subject j at the (k+1)th iteration; The unit price proposed by subject j for electricity trading with subject i at the (k+1)th iteration; The convergence status can be determined using the following formula: Where δ represents the upper limit of convergence, and the algorithm converges if the above formula is satisfied. If k>k max This indicates that the algorithm does not converge, where k max This is the maximum number of iterations set to limit the iteration time.

8. A local area network and main network globally optimal operation device based on asymmetric Nash negotiation, characterized in that, The device includes: The interaction characteristic analysis module is configured to determine the type of local power grid based on its functional characteristics and structural features, and to analyze the interaction characteristics of different types of local power grids under various operating modes; wherein, the types of local power grids include residential areas, high-energy-consuming industrial areas, and commercial areas; The operation model construction module is configured to construct a coordinated optimization operation model between the local power grid and the main grid. The coordinated optimization operation model aims to optimize the overall system operating cost, and the power transactions between local power grids and between the local power grid and the main grid are based on the interaction cost coefficient. The scheme determination module is configured to calculate the optimal interactive power between local power grids and between the local power grid and the main grid based on the coordinated optimization operation model, and determine the globally optimal operation scheme. The benefit allocation module is configured to construct a multi-stakeholder benefit allocation model based on asymmetric Nash negotiation theory. The multi-stakeholders include local power grids and the main grid. The multi-stakeholder benefit allocation model allocates benefits by quantifying the bargaining power of each stakeholder through the introduction of a comprehensive contribution rate. The comprehensive contribution rate is determined based on a nonlinear energy sharing mapping contribution factor and a demand response contribution factor. The operation strategy adjustment module is configured to compensate the operating costs of each entity based on the multi-entity benefit allocation model and the global optimal operation scheme, and adjust the operation strategies of the local power grid and the main grid.

9. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes the computer execution instructions stored in the memory to implement the global optimal operation method for local area networks and main networks based on asymmetric Nash negotiation as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the global optimal operation method for local area networks and main networks based on asymmetric Nash negotiation as described in any one of claims 1-7.

Images