Dynamic correction type source-load model data double-driven power grid coordinated control method and system

Abstract

The application discloses a dynamic correction type source-load model data double driving power grid coordinated control method and system, and comprises the following steps: building a source-load model for power sources and loads in a power grid; combining and weighting physical model parameters in the source-load model to obtain a double driving source-load model; constructing an elasticity coefficient of the physical model parameters to express the influence of a power price change; optimizing the elasticity coefficient of the physical model parameters to obtain an optimal elasticity coefficient; obtaining the weight of the influence of the power price change on each physical model parameter according to the optimal elasticity coefficient obtained by optimization, and estimating the optimal source-load response in the current situation by the double driving source-load model to control the state of the power sources and loads in the power grid. The application aims to realize the coordinated optimization control of source-grid-load-storage by combining the time-of-use price mechanism, so that the power grid coordinated control can adapt to market changes and improve the power grid regulation and control efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid dispatching, and in particular to a method and system for dynamically corrected source-load model data dual-driven power grid coordination control. Background Art

[0002] Against the backdrop of today's rapidly evolving power system, traditional source-load models face challenges in emerging market environments. With the widespread integration of distributed power sources (such as solar photovoltaic and wind power generation), electric vehicles, and large-scale behind-the-meter resources, the operation and management of distribution networks must further adapt to the volatility and uncertainty of these resources. The dynamic nature of these resources requires distribution networks to possess greater flexibility and adaptability to effectively address dynamic fluctuations between supply and demand, ensuring grid stability and reliable power supply. With the ongoing reform of the power market, market mechanisms and price signals have also had a significant impact on the operation and management of distribution networks. In particular, the introduction of time-of-use pricing mechanisms has provided an effective economic means to incentivize users to participate in demand response and optimize electricity consumption. Time-of-use pricing mechanisms can better incentivize users to increase their electricity consumption during periods of low demand and reduce it during periods of high demand, thereby reducing peak-to-valley fluctuations in the power grid and improving its operational efficiency and economic viability. However, existing source-load models often lack sensitivity and adaptability to market factors, making them difficult to meet the flexibility and accuracy requirements of modern power systems. Traditional models, often based on static data and simplified assumptions, fail to fully capture the impact of market changes on source-load behavior, limiting their effectiveness in actual grid dispatch. In this market environment, power system operators face the challenge of designing source-load models that can both adapt to market changes and improve grid regulation efficiency. Furthermore, how to achieve coordinated and optimized control of source, grid, load, and storage, combined with time-of-use electricity pricing, is a key issue currently facing power system operators. Summary of the Invention

[0003] The technical problem to be solved by the present invention is as follows: In response to the above-mentioned problems of the prior art, a method and system for power grid coordinated control based on a dynamic correction type source-load model data dual-drive is provided. The present invention aims to realize the coordinated optimization control of source-grid-load-storage in combination with the time-of-use electricity price mechanism, so that the coordinated control of the power grid can adapt to market changes and improve the efficiency of power grid regulation.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0005] A dynamically modified source-load model data dual-driven power grid coordinated control method includes the following steps:

[0006] S1, obtain the physical characteristic parameters of power sources and loads in the power grid and build source-load models for them;

[0007] S2, combining and weighting the physical model parameters in the source-load model to obtain a dual-driven source-load model, wherein the combined weighting refers to multiplying the physical model parameters by weights affected by changes in electricity prices, so that the estimated source-load response obtained by the dual-driven source-load model is driven by both the physical model parameters and the electricity price changes.

[0008] S3, constructing elasticity coefficients for each physical model parameter to express the effect of electricity price changes, wherein the elasticity coefficients are multiplied by the electricity price changes to obtain weights of the physical model parameters affected by the electricity price changes, and optimizing the elasticity coefficients of the physical model parameters until the error between the estimated source-load response obtained by the dual-drive source-load model and the actual source-load response is less than a preset threshold;

[0009] S4, in response to the current electricity price changes, the weights of the physical model parameters affected by the electricity price changes are obtained based on the optimal elastic coefficient obtained through optimization, and the current optimal source-load response is estimated through the dual-drive source-load model;

[0010] S5: Send the current optimal source-load response to control the status of power sources and loads in the power grid.

[0011] Optionally, the power source in step S1 refers to power generation equipment in the power grid, and the power generation equipment includes part or all of thermal power plants, hydroelectric power stations, wind power stations and solar power stations, and the load includes part or all of industrial loads, commercial loads, residential loads and agricultural loads in the power grid.

[0012] Optionally, when constructing a source-load model for the power source and load in step S1, the function expression of the source-load model constructed for the thermal power plant is:

[0013] ,

[0014] In the above formula, Represents the output power of a thermal power plant, and is used as the source-load response of the source-load model of the thermal power plant; represents the design thermal efficiency, Indicates the actual fuel consumption, represents the designed fuel consumption, The calorific value of the fuel, Indicates the quality of the fuel;

[0015] The functional expression of the source-load model constructed for the hydroelectric power station is:

[0016] ,

[0017] in, Represents the output power of the hydroelectric power station, which is used as the source load response of the source load model of the hydroelectric power plant; represents the density of water, represents the acceleration due to gravity, Indicates the water flow velocity, It represents the cross-sectional area of ​​the water flow through the turbine. Indicates the height of the water drop. Indicates the efficiency of the generator set;

[0018] The function expression of the physical model built for the wind power station is:

[0019] ,

[0020] in, Represents the output power of the wind power station, which is used as the source load response of the source load model of the wind power plant; is the wind speed, Indicates the starting wind speed. is the air density, represents the area swept by the blade, represents the wind energy conversion efficiency, is the wind speed ratio, represents the efficiency of the wind turbine generator set, Indicates the rated wind speed, Indicates the rated output power of the wind turbine generator set. Indicates the cut-out wind speed. When the wind speed exceeds the cut-out wind speed and is greater than the rated wind speed, the output power remains unchanged.

[0021] The function expression of the source-load model constructed for the solar power station is:

[0022] ,

[0023] in, Represents the output power of the solar power station, which is used as the source-load response of the source-load model of the solar power station; represents the solar radiation intensity, and Represent the tilt angle and azimuth of the photovoltaic panel, represents the total area of ​​photovoltaic panels, Indicates the efficiency of the solar power generator set, represents the temperature coefficient, and Represents actual temperature and reference temperature respectively.

[0024] Optionally, when constructing a source-load model for the power source and the load in step S1, the function expression of the source-load model constructed for the load is:

[0025] ,

[0026] in, Indicates the power demand of the load, used as the source-load response of the source-load model of the load; Indicates the base load. Different types of loads have different base loads. Indicates fluctuating load, For time.

[0027] Optionally, before combining and weighting the physical model parameters in the source-load model to obtain the dual-driven source-load model in step S2, it also includes using a regression analysis method to obtain the mathematical relationship between each physical model parameter and the source-load response data in the source-load model, wherein the mathematical relationship is a correlation or the coefficient of a fitting polynomial, and the physical model parameters and the source-load response data are sorted in descending order according to the mathematical relationship between them, and multiple key physical model parameters before sorting are selected; combining and weighting the physical model parameters in the source-load model to obtain the dual-driven source-load model in step S2 means combining and weighting the key physical model parameters in the source-load model to obtain the dual-driven source-load model.

[0028] Optionally, step S3 includes:

[0029] S3.1, import historical electricity price data at different times, actual physical model parameter data, and actual source-load response;

[0030] S3.2, construct an electricity price elasticity correction matrix E to record the physical model parameters to construct an elasticity coefficient that is affected by electricity price changes. The electricity price elasticity correction matrix E is a matrix of size N×M, where N represents the number of physical model parameters in the source-load model, and M represents the number of time periods in which electricity prices change over time. ij Representative j The price of the time period i The elastic coefficients of the source load model parameters;

[0031] S3.3, using a specified intelligent optimization algorithm to randomly search the elasticity coefficients in the electricity price elasticity correction matrix E to obtain multiple sets of electricity price elasticity correction matrices E;

[0032] S3.4. For each set of electricity price elasticity correction matrix E, multiply the searched elasticity coefficient by the electricity price change to obtain the weight of the physical model parameters affected by the electricity price change. Substitute the weight of the physical model parameters affected by the electricity price change and the actual physical model parameter data into the estimated source-load response obtained by the dual-drive source-load model. The optimal electricity price elasticity correction matrix E is obtained by minimizing the deviation between the estimated source-load response obtained by the dual-drive source-load model and the actual source-load response.

[0033] S3.5, determine whether the error index between the estimated source-load response and the actual source-load response obtained by the dual-drive source-load model corresponding to the optimal electricity price elasticity correction matrix E is less than the preset value. If it is less than the preset value, it is determined that the optimization of the elasticity coefficient of the physical model parameters is completed, and jump to step S4; otherwise, jump to step S3.3 to continue iterative optimization.

[0034] Optionally, the error index between the estimated source-load response and the actual source-load response in step S3.5 is one of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and Nash efficiency coefficient (NSE).

[0035] In addition, the present invention also provides a dynamically modified source-load model data dual-drive power grid coordination control system, including an interconnected microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the dynamically modified source-load model data dual-drive power grid coordination control method.

[0036] In addition, the present invention also provides a computer-readable storage medium, which stores a computer program or instruction, and the computer program or instruction is programmed or configured to execute the dynamic correction source-load model data dual-drive power grid coordinated control method through a processor.

[0037] In addition, the present invention also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the dynamic modified source-load model data dual-drive power grid coordinated control method through a processor.

[0038] Compared with the prior art, the present invention mainly has the following advantages: with the continuous deepening of the electricity market, the accuracy of the source-load model is crucial to the efficiency and accuracy of power grid dispatching. The core of the present invention is to propose a dual-driven modeling method of model data that combines theoretical modeling with statistical analysis. The method obtains a dual-driven source-load model by combining and weighting the physical model parameters in the source-load model, so that the estimated source-load response obtained by the dual-driven source-load model is driven by both the physical model parameters and the electricity price changes. Elasticity coefficients expressing the influence of electricity price changes are constructed for the physical model parameters respectively. The elasticity coefficients are used to multiply the electricity price changes to obtain the weights of the physical model parameters affected by the electricity price changes, and the physical model parameters are weighted. The elastic coefficient of the number is optimized until the error between the estimated source-load response obtained by the dual-drive source-load model and the actual source-load response is less than a preset threshold. For the current electricity price change, the weights of each physical model parameter affected by the electricity price change are obtained according to the optimal elastic coefficient obtained by optimization, and the current optimal source-load response is estimated by the dual-drive source-load model. The current optimal source-load response is issued to control the status of power supply and load in the power grid, so that the source-load model can be dynamically corrected by market factors to adapt to changes in the electricity market. It can effectively realize the coordinated optimization control of source-grid-load-storage in combination with the time-of-use electricity price mechanism, so that the coordinated control of the power grid can not only adapt to market changes but also improve the efficiency of power grid regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] Figure 1 Schematic diagram of the basic process of the method of the embodiment of the present invention.

[0040] Figure 2 Schematic diagram of the power grid topology in an embodiment of the present invention.

[0041] Figure 3 This is a flow chart of dynamically correcting the source-load model in an embodiment of the present invention. DETAILED DESCRIPTION

[0042] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0043] With the continuous deepening of the electricity market, the accuracy of the source-load model is crucial to the efficiency and accuracy of grid dispatch. In view of the fact that the existing source-load model lacks sensitivity and adaptability to market factors and is difficult to meet the requirements of flexibility and accuracy of modern power systems, the core of this invention is to propose a model data dual-driven modeling method that combines theoretical modeling and statistical analysis. This method dynamically corrects the source-load model through market factors to adapt to changes in the electricity market, solves the limitations of the existing source-load model in market adaptability, and improves the model's response speed and accuracy to market changes, so as to adapt to the new electricity market environment with a high proportion of distributed resources access, so as to better integrate and optimize various resources in the electricity market and realize the economical and efficient operation and sustainable development of the power grid. Figure 1 As shown, the dynamic correction type source-load model data dual-drive power grid coordinated control method of this embodiment includes the following steps:

[0044] S1, obtain the physical characteristic parameters of power sources and loads in the power grid and build source-load models for them;

[0045] S2, combining and weighting the physical model parameters in the source-load model to obtain a dual-driven source-load model, wherein the combined weighting refers to multiplying the physical model parameters by weights affected by changes in electricity prices, so that the estimated source-load response obtained by the dual-driven source-load model is driven by both the physical model parameters and the electricity price changes.

[0046] S3, constructing elasticity coefficients for each physical model parameter to express the effect of electricity price changes, wherein the elasticity coefficients are multiplied by the electricity price changes to obtain weights of the physical model parameters affected by the electricity price changes, and optimizing the elasticity coefficients of the physical model parameters until the error between the estimated source-load response obtained by the dual-drive source-load model and the actual source-load response is less than a preset threshold;

[0047] S4, in response to the current electricity price changes, the weights of the physical model parameters affected by the electricity price changes are obtained based on the optimal elastic coefficient obtained through optimization, and the current optimal source-load response is estimated through the dual-drive source-load model;

[0048] S5: Send the current optimal source-load response to control the status of power sources and loads in the power grid.

[0049] As an optional implementation method, the dynamic correction type source-load model data dual-driven grid coordination control method of this embodiment includes a data collection and model building module, a market factor correction module and a model optimization module. The data collection and model building module is responsible for collecting the physical characteristics and historical data of the source and load, and constructing the source and load model based on theoretical modeling and statistical analysis; the market factor correction module dynamically corrects the constructed model according to market changes to construct a dual-driven source and load model; the model evaluation and optimization module is responsible for optimizing the elasticity coefficient to ensure the accuracy and effectiveness of the dual-driven source and load model and continuously optimize the dual-driven source and load model to meet the needs of grid dispatching. The application of the dynamic correction type source and load model data dual-driven grid coordination control method of this embodiment will provide a highly efficient source and load modeling and grid coordination control method for grid dispatching, which can adapt to market changes, optimize grid dispatching efficiency and accuracy, and has important practical application value and market prospects.

[0050] The power source in step S1 of this embodiment refers to the power generation equipment in the power grid. Such power generation equipment includes some or all of thermal power plants, hydroelectric power plants, wind power plants, and solar power plants. Each type of power generation equipment has unique physical characteristics and operating parameters. The load includes some or all of the industrial load, commercial load, residential load, and agricultural load in the power grid. When obtaining the physical characteristic parameters of the power source and load in the power grid in step S1, the type of the power source and load in the power grid can be obtained and their basic parameters, such as capacity, efficiency, and operating status, can be recorded. Figure 2 This is a schematic diagram of the power grid topology in an embodiment of the present invention, where 1 to 33 are nodes in the power grid. The power grid is connected to the upper power grid through a transformer. Wind power stations (represented by wind turbines) and solar power stations (represented by the sun) and loads (represented by electric vehicles and batteries) are distributed in the power grid. When constructing the source-load model for the power source and load in step S1 of this embodiment, mathematical formulas are used to express the physical behavior of the source and load and the operating laws of the power grid to ensure the accuracy and predictive ability of the model. The physical model is used and the source-load response of the corresponding source and load is obtained based on the obtained physical characteristics. In this embodiment, the function expression of the source-load model constructed for the thermal power plant is:

[0051] ,

[0052] In the above formula, Represents the output power of a thermal power plant, and is used as the source-load response of the source-load model of the thermal power plant; represents the design thermal efficiency, Indicates the actual fuel consumption, represents the designed fuel consumption, The calorific value of the fuel, Indicates the quality of the fuel.

[0053] In this embodiment, the function expression of the source-load model constructed for the hydropower station is:

[0054] ,

[0055] in, Represents the output power of the hydroelectric power station, which is used as the source load response of the source load model of the hydroelectric power plant; represents the density of water, represents the acceleration due to gravity, Indicates the water flow velocity, It represents the cross-sectional area of ​​the water flow through the turbine. Indicates the height of the water drop. Indicates the efficiency of the generator set.

[0056] In this embodiment, the function expression of the physical model constructed for the wind power station is:

[0057] ,

[0058] in, Represents the output power of the wind power station, which is used as the source load response of the source load model of the wind power plant; is the wind speed, Indicates the starting wind speed. is the air density, represents the area swept by the blade, represents the wind energy conversion efficiency, is the wind speed ratio, represents the efficiency of the wind turbine generator set, Indicates the rated wind speed, Indicates the rated output power of the wind turbine generator set. Indicates the cut-out wind speed. When the wind speed exceeds the cut-out wind speed and is greater than the rated wind speed, the output power remains unchanged.

[0059] In this embodiment, the function expression of the source-load model constructed for the solar power station is:

[0060] ,

[0061] in, Represents the output power of the solar power station, which is used as the source-load response of the source-load model of the solar power station; represents the solar radiation intensity, and Represent the tilt angle and azimuth of the photovoltaic panel, represents the total area of ​​photovoltaic panels, Indicates the efficiency of the solar power generator set, represents the temperature coefficient, and Represents actual temperature and reference temperature respectively.

[0062] When constructing a source-load model for the power source and the load in step S1 of this embodiment, the function expression of the source-load model constructed for the load is:

[0063] ,

[0064] in, Indicates the power demand of the load, used as the source-load response of the source-load model of the load; Indicates the base load. Different types of loads have different base loads. Indicates fluctuating load, For time.

[0065] In step S2 of this embodiment, before combining and weighting the physical model parameters in the source-load model to obtain the dual-driven source-load model, the method further includes obtaining a mathematical relationship between each physical model parameter and the source-load response data in the source-load model using a regression analysis method, wherein the mathematical relationship is a correlation or a coefficient of a fitting polynomial, and sorting the physical model parameters and the source-load response data in descending order according to the mathematical relationship between the physical model parameters and the source-load response data, and selecting multiple key physical model parameters before sorting; combining and weighting the physical model parameters in the source-load model to obtain the dual-driven source-load model in step S2 means combining and weighting the key physical model parameters in the source-load model to obtain the dual-driven source-load model.

[0066] like Figure 3 As shown, step S3 of this embodiment includes:

[0067] S3.1, import historical electricity price data at different times, actual physical model parameter data, and actual source-load response;

[0068] S3.2, construct an electricity price elasticity correction matrix E to record the physical model parameters to construct an elasticity coefficient that is affected by electricity price changes. The electricity price elasticity correction matrix E is a matrix of size N×M, where N represents the number of physical model parameters in the source-load model, and M represents the number of time periods in which electricity prices change over time. ij Representative j The price of the time period i The elastic coefficients of the source load model parameters;

[0069] S3.3, using a specified intelligent optimization algorithm to randomly search the elasticity coefficients in the electricity price elasticity correction matrix E to obtain multiple sets of electricity price elasticity correction matrices E;

[0070] S3.4. For each set of electricity price elasticity correction matrix E, multiply the searched elasticity coefficient by the electricity price change to obtain the weight of the physical model parameters affected by the electricity price change. Substitute the weight of the physical model parameters affected by the electricity price change and the actual physical model parameter data into the estimated source-load response obtained by the dual-drive source-load model. The optimal electricity price elasticity correction matrix E is obtained by minimizing the deviation between the estimated source-load response obtained by the dual-drive source-load model and the actual source-load response.

[0071] S3.5, determine whether the error index between the estimated source-load response and the actual source-load response obtained by the dual-drive source-load model corresponding to the optimal electricity price elasticity correction matrix E is less than the preset value. If it is less than the preset value, it is determined that the optimization of the elasticity coefficient of the physical model parameters is completed, and jump to step S4; otherwise, jump to step S3.3 to continue iterative optimization.

[0072] The historical electricity price data, actual physical model parameter data, and actual source-load response data at different times in step S3.1 of this embodiment involve long-term monitoring of power generation equipment and loads in the power grid to collect historical operating data. This includes, but is not limited to, the output power, operating status, maintenance records, fuel consumption of each power generation equipment, as well as the power demand, power consumption patterns, and peak and off-peak periods of each load point. This collection process utilizes devices such as smart meters, supervisory control and data acquisition (SCADA), and remote terminal units (RTUs) to achieve automated data collection.

[0073] The error metric between the estimated source-load response and the actual source-load response in step S3.5 is used to measure the consistency between the model prediction value and the actual observation value. In this embodiment, the error metric between the estimated source-load response and the actual source-load response in step S3.5 is one of the mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), coefficient of determination (R²), and Nash efficiency coefficient (NSE).

[0074] It should be noted that after the current optimal source-load response is issued in step S5 to control the status of the power source and load in the power grid, suggestions for model improvement can be further proposed based on the model evaluation results, such as adding training data, adjusting model parameters, or adopting a more complex model structure.

[0075] In summary, in current power system operations, with the deepening of market-oriented approaches and the transformation of energy structures, distribution networks face the dual challenges of increasing uncertainty in source and load dynamics and the dynamics of market factors. This present invention, focusing on distribution networks in this new power market environment, considers the physical characteristics of the power grid and market operating mechanisms. By comprehensively analyzing multi-dimensional information such as power grid operating data, source and load characteristics, power market price signals, and user response behavior, it constructs a data-driven modeling method and system for source and load models that are modified based on market factors. This embodiment utilizes advanced data analysis techniques and machine learning algorithms to deeply mine and intelligently process source and load data, enabling real-time response to market changes and dynamic adjustment of the source and load model. This system not only provides grid operators with accurate source and load forecasting and scheduling strategies, but also offers decision support to market participants, optimizing their market participation strategies. Through the implementation of this invention, grid operators can more flexibly respond to market changes, achieve optimal source and load allocation and scheduling, and improve the economic efficiency and reliability of grid operation. This embodiment constructs a highly flexible and adaptable source and load model by comprehensively considering power system operating data, electricity price signals, and the actual physical characteristics of each source and load. This model can intelligently analyze and predict changes in power sources and loads within the power grid, providing scientific decision-making support for power grid operators. Specifically, the method of this embodiment can dynamically respond to fluctuations in electricity prices by adjusting the power source and load model through a price elasticity correction matrix, achieving adaptive modeling of each power source and load within the power grid. This not only improves the operational efficiency of the power grid but also enhances its adaptability to uncertainties. Furthermore, this embodiment provides a market factor correction mechanism that can rapidly adjust the power source and load model based on real-time market information, ensuring that power grid dispatch strategies remain consistent with the market environment. This mechanism is particularly well-suited to the rapidly changing and highly competitive nature of the current power market. Through the implementation of this embodiment, power grid operators can more accurately predict and respond to various power grid operational situations, achieving efficient resource utilization and stable power grid operation, and promoting the healthy development of the entire power market. This embodiment has the following key advantages: 1. Market adaptability: This embodiment can respond in real time to market changes, such as electricity price fluctuations and shifts in supply and demand, by adjusting the power source and load model through a dynamic correction mechanism, thereby improving the flexibility and cost-effectiveness of power grid dispatch. 2. Accuracy: By using advanced data analysis and machine learning techniques, this embodiment can more accurately fit source-load behavior, thereby improving the accuracy and reliability of grid dispatch. 3. Resource optimization configuration: This embodiment optimizes the configuration of grid resources through a data-driven dual-modeling approach, achieves efficient management of source-load balance, and improves resource utilization efficiency. 4. Flexibility and scalability: The model design takes into account the diversity of different types of source-load characteristics and can adapt to grid systems of different sizes and structures, with good flexibility and scalability.5. User Participation Incentives: This embodiment, combined with a time-of-use electricity price mechanism, provides effective incentives for users to participate in demand response, promotes the active participation of user-side resources, and enhances the overall dispatching capabilities of the power grid. 6. Economy and Efficiency: By optimizing the dispatching strategy, this embodiment helps reduce power grid operating costs while improving the efficiency of power grid operation, achieving a win-win situation in terms of economic and environmental benefits. 7. Technology Integration: This embodiment integrates multiple modeling technologies and market analysis tools to form a comprehensive decision support system, providing a comprehensive solution for power grid operators.

[0076] In addition, this embodiment also provides a dynamically corrected source-load model data dual-drive grid coordination control system, including an interconnected microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the dynamically corrected source-load model data dual-drive grid coordination control method.

[0077] In addition, this embodiment also provides a computer-readable storage medium, which stores a computer program or instruction, and the computer program or instruction is programmed or configured to execute the dynamic correction source-load model data dual-drive power grid coordination control method through a processor.

[0078] In addition, this embodiment also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the dynamic modified source-load model data dual-drive power grid coordinated control method through a processor.

[0079] Those skilled in the art should understand that the technical solutions provided by the embodiments of the present application may be in the form of methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, may be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including the instruction device, which implements the function specified in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0080] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A dynamic correction type source-load model data dual-drive power grid coordinated control method, characterized in that: The steps include: S1, obtain the physical characteristic parameters of power sources and loads in the power grid and build source-load models for them; S2, combining and weighting the physical model parameters in the source-load model to obtain a dual-driven source-load model, wherein the combined weighting refers to multiplying the physical model parameters by weights affected by changes in electricity prices, so that the estimated source-load response obtained by the dual-driven source-load model is driven by both the physical model parameters and the electricity price changes. S3, constructing elasticity coefficients for each physical model parameter to express the effect of electricity price changes, wherein the elasticity coefficients are multiplied by the electricity price changes to obtain weights of the physical model parameters affected by the electricity price changes, and optimizing the elasticity coefficients of the physical model parameters until the error between the estimated source-load response obtained by the dual-drive source-load model and the actual source-load response is less than a preset threshold; S4, in response to the current electricity price changes, the weights of the physical model parameters affected by the electricity price changes are obtained based on the optimal elastic coefficient obtained through optimization, and the current optimal source-load response is estimated through the dual-drive source-load model; S5, sending the current optimal source-load response to control the state of power sources and loads in the power grid; The power source in step S1 refers to power generation equipment in the power grid, which includes part or all of thermal power plants, hydropower stations, wind power stations, and solar power stations; the load includes part or all of industrial loads, commercial loads, residential loads, and agricultural loads in the power grid; When constructing the source-load model for the power source and load in step S1, the function expression of the source-load model constructed for the thermal power plant is: ， In the above formula, Represents the output power of a thermal power plant, and is used as the source-load response of the source-load model of the thermal power plant; represents the design thermal efficiency, Indicates the actual fuel consumption, represents the designed fuel consumption, The calorific value of the fuel, Indicates the quality of the fuel; The functional expression of the source-load model constructed for the hydroelectric power station is: ， in, Represents the output power of the hydroelectric power station, which is used as the source load response of the source load model of the hydroelectric power plant; represents the density of water, represents the acceleration due to gravity, Indicates the water flow velocity, It represents the cross-sectional area of ​​the water flow through the turbine. Indicates the height of the water drop. Indicates the efficiency of the generator set; The function expression of the physical model built for the wind power station is: , in, Represents the output power of the wind power station, which is used as the source load response of the source load model of the wind power plant; is the wind speed, Indicates the starting wind speed. is the air density, represents the area swept by the blade, represents the wind energy conversion efficiency, is the wind speed ratio, represents the efficiency of the wind turbine generator set, Indicates the rated wind speed, Indicates the rated output power of the wind turbine generator set. Indicates the cut-out wind speed. When the wind speed exceeds the cut-out wind speed and is greater than the rated wind speed, the output power remains unchanged. The function expression of the source-load model constructed for the solar power station is: ， in, Represents the output power of the solar power station, which is used as the source-load response of the source-load model of the solar power station; represents the solar radiation intensity, and Represent the tilt angle and azimuth of the photovoltaic panel, represents the total area of ​​photovoltaic panels, Indicates the efficiency of the solar power generator set, represents the temperature coefficient, and Represents actual temperature and reference temperature respectively.

2. The method for coordinated control of a power grid based on a dynamic modified source-load model data dual drive according to claim 1, characterized in that: When constructing the source-load model for the power source and the load in step S1, the function expression of the source-load model constructed for the load is: ， in, Indicates the power demand of the load, used as the source-load response of the source-load model of the load; Indicates the base load. Different types of loads have different base loads. Indicates fluctuating load, For time.

3. The method for coordinated control of a power grid based on a dynamic modified source-load model data dual drive according to claim 1, characterized in that: Before combining and weighting the physical model parameters in the source-load model to obtain the dual-driven source-load model in step S2, the method also includes obtaining the mathematical relationship between the various physical model parameters and the source-load response data in the source-load model using a regression analysis method, wherein the mathematical relationship is a correlation or a coefficient of a fitting polynomial, and sorting the various physical model parameters and the source-load response data in descending order according to the mathematical relationship between the physical model parameters and the source-load response data, and selecting multiple key physical model parameters before sorting; combining and weighting the physical model parameters in the source-load model to obtain the dual-driven source-load model in step S2 means combining and weighting the key physical model parameters in the source-load model to obtain the dual-driven source-load model.

4. The method for coordinated control of a power grid using a dynamic modified source-load model data dual drive according to claim 1, characterized in that: Step S3 includes: S3.1, import historical electricity price data at different times, actual physical model parameter data, and actual source-load response; S3.2, construct an electricity price elasticity correction matrix E to record the physical model parameters to construct an elasticity coefficient that is affected by electricity price changes. The electricity price elasticity correction matrix E is a matrix of size N×M, where N represents the number of physical model parameters in the source-load model, and M represents the number of time periods in which electricity prices change over time. ij Representative j The price of the time period i The elastic coefficients of the source load model parameters; S3.3, using a specified intelligent optimization algorithm to randomly search the elasticity coefficients in the electricity price elasticity correction matrix E to obtain multiple sets of electricity price elasticity correction matrices E; S3.

4. For each set of electricity price elasticity correction matrix E, multiply the searched elasticity coefficient by the electricity price change to obtain the weight of the physical model parameters affected by the electricity price change. Substitute the weight of the physical model parameters affected by the electricity price change and the actual physical model parameter data into the estimated source-load response obtained by the dual-drive source-load model. The optimal electricity price elasticity correction matrix E is obtained by minimizing the deviation between the estimated source-load response obtained by the dual-drive source-load model and the actual source-load response. S3.5, determine whether the error index between the estimated source-load response and the actual source-load response obtained by the dual-drive source-load model corresponding to the optimal electricity price elasticity correction matrix E is less than the preset value. If it is less than the preset value, it is determined that the optimization of the elasticity coefficient of the physical model parameters is completed, and jump to step S4; otherwise, jump to step S3.3 to continue iterative optimization.

5. The method for coordinated control of a power grid based on a dynamic modified source-load model data dual drive according to claim 4 is characterized in that: The error index between the estimated source-load response and the actual source-load response in step S3.5 is one of the mean square error (MSE), the root mean square error (RMSE), the mean absolute error (MAE), the coefficient of determination (R²), and the Nash efficiency coefficient (NSE).

6. A dynamically modified source-load model data dual-drive power grid coordination control system, comprising a microprocessor and a memory connected to each other, characterized in that: The microprocessor is programmed or configured to execute the dynamic modified source-load model data dual-drive power grid coordinated control method according to any one of claims 1 to 5.

7. A computer-readable storage medium having a computer program or instruction stored therein, characterized in that: The computer program or instruction is programmed or configured to execute the dynamic correction type source-load model data dual-drive power grid coordinated control method according to any one of claims 1 to 5 through a processor.

8. A computer program product comprising a computer program or instructions, characterized in that The computer program or instruction is programmed or configured to execute the dynamic correction type source-load model data dual-drive power grid coordinated control method according to any one of claims 1 to 5 through a processor.

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