Dynamic correction type source load model data dual-drive power grid coordination control method and system

Abstract

The invention discloses a dynamic correction type source load model data dual-drive power grid coordination control method and system. The method comprises the following steps: building source load models for a power supply and a load in a power grid; performing combination weighting on physical model parameters in the source load model to obtain a dual-drive source load model, constructing an elastic coefficient expressing the influence of electricity price change for the physical model parameters, and optimizing the elastic coefficient of the physical model parameters to obtain an optimal elastic coefficient; and for the current electricity price change, the weight of each physical model parameter influenced by the electricity price change is obtained according to the optimal elastic coefficient obtained through optimization, and the current optimal source load response is estimated through the dual-drive source load model and is issued to control the states of a power supply and a load in the power grid. The invention aims to realize source-grid-load-storage coordinated optimization control in combination with a time-of-use electricity price mechanism, so that power grid coordinated control can adapt to market changes, and the power grid regulation and control efficiency can be improved.

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 coordinated control of a power grid driven by a dynamic correction source-load model data dual drive. Background Art

[0002] In the context of the rapid development of today's power system, traditional source-load models face challenges in the emerging market environment. With the widespread access of distributed power sources (such as solar photovoltaics, wind power generation), electric vehicles, large-scale user-side resources and other devices, the operation and management of distribution networks need to further adapt to the volatility and uncertainty of these resources. The dynamic characteristics of these resources require that the distribution network must have higher flexibility and adaptability to effectively respond to the dynamic changes between supply and demand, and ensure the stability of the power grid and the reliability of power supply. With the continuous deepening of power market reform, market mechanisms and price signals have also had an important impact on the operation and management of distribution networks. In particular, the introduction of the time-of-use electricity price mechanism provides an effective economic means to encourage users to participate in demand response and optimize electricity consumption behavior. The time-of-use electricity price mechanism can better encourage users to increase electricity consumption during periods of low electricity demand and reduce electricity consumption during periods of high electricity demand, thereby reducing the peak-to-valley difference of the power grid and improving the operation efficiency and economy of the power grid. However, the existing source-load models often lack sensitivity and adaptability to market factors, and it is difficult to meet the requirements of modern power systems for flexibility and accuracy. Traditional models are usually built based on static data and simplified assumptions, which cannot fully capture the impact of market changes on source-load behavior, limiting the application effect of the model in actual power grid dispatching. In this market environment, power system operators are faced with the challenge of how to design a source-load model that can adapt to market changes and improve the efficiency of power grid regulation. In addition, how to combine the time-of-use electricity price mechanism to achieve coordinated optimization control of source-grid-load-storage is also a key issue that current power system operators need to solve. Summary of the invention

[0003] Technical problem to be solved by the present invention: In view of the above-mentioned problems of the prior art, a dynamically corrected source-load model data dual-driven power grid coordinated control method and system are 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 not only adapt to market changes but also 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: A dynamically modified source-load model data dual-drive power grid coordinated control method comprises the following steps: S1, obtaining the physical characteristic parameters of the power source and load in the power grid and building a source-load model for the power source and load; 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 physical model parameters and electricity price changes and changes; S3, constructing elastic coefficients expressing the influence of electricity price changes for physical model parameters respectively, wherein the elastic coefficients are used to multiply the electricity price changes to obtain weights of the physical model parameters influenced by the electricity price changes, and optimizing the elastic 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 view of the current electricity price changes, the weights of various physical model parameters affected by the electricity price changes are obtained according to the optimal elastic coefficient obtained by 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 supply and load in the power grid.

[0005] Optionally, the power source in step S1 refers to power generation equipment in the power grid, which includes part or all of thermal power plants, hydroelectric power plants, wind power plants and solar power plants, and the load includes part or all of industrial loads, commercial loads, residential loads and agricultural loads in the power grid.

[0006] 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 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 a source-load model of a 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, and 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 flowing through the turbine. 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, and 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 functional expression of the source-load model constructed for the solar power station is: , in, represents the output power of the solar power station, and is used as the source-load response of the source-load model of the solar power station; represents the intensity of solar radiation, and Respectively represent the tilt angle and azimuth of the photovoltaic panel, represents the total area of ​​photovoltaic panels, Represents the efficiency of the solar power generator set, represents the temperature coefficient, and Represent the actual temperature and the reference temperature respectively.

[0007] 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: , in, Represents 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. represents fluctuating load, For time.

[0008] 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 a 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.

[0009] Optionally, 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 elastic coefficients 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 the electricity price changes over time. Any element E in the i-th row and j-th column 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, the weight of the physical model parameters affected by the electricity price change is obtained by multiplying the searched elasticity coefficient by the electricity price change, and the weight of the physical model parameters affected by the electricity price change and the actual physical model parameter data are substituted into the estimated source-load response obtained by the dual-drive source-load model, and the optimal electricity price elasticity correction matrix E is obtained by taking the minimum deviation between the estimated source-load response obtained by the dual-drive source-load model and the actual source-load response as the fitness; 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.

[0010] 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, determination coefficient R² and Nash efficiency coefficient NSE.

[0011] In addition, the present invention also provides a dynamically corrected source-load model data dual-drive power grid coordination control system, comprising an interconnected microprocessor and a memory, wherein the microprocessor is programmed or configured to execute the dynamically corrected source-load model data dual-drive power grid coordination control method.

[0012] 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 modified source-load model data dual-drive power grid coordination control method through a processor.

[0013] 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.

[0014] 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 model data dual-driven modeling method 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 the 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 state 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, and the coordinated optimization control of source-grid-load-storage can be effectively realized 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

[0015] Figure 1 Schematic diagram of the basic flow of the method of the embodiment of the present invention.

[0016] Figure 2 Schematic diagram of the power grid topology structure in an embodiment of the present invention.

[0017] Figure 3The flowchart of dynamically correcting the source-load model in an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] As the electricity market continues to deepen, 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 the present 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 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: S1, obtaining the physical characteristic parameters of the power source and load in the power grid and building a source-load model for the power source and load; 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 physical model parameters and electricity price changes and changes; S3, constructing elastic coefficients expressing the influence of electricity price changes for physical model parameters respectively, wherein the elastic coefficients are used to multiply the electricity price changes to obtain weights of the physical model parameters influenced by the electricity price changes, and optimizing the elastic 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 view of the current electricity price changes, the weights of various physical model parameters affected by the electricity price changes are obtained according to the optimal elastic coefficient obtained by 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 supply and load in the power grid.

[0020] As an optional implementation method, the dynamically corrected source-load model data dual-driven power grid coordinated 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 load, and constructing the source 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 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 load model and continuously optimize the dual-driven source load model to meet the needs of power grid dispatching. The application of the dynamically corrected source load model data dual-driven power grid coordinated control method of this embodiment will provide an efficient source load modeling and power grid coordinated control method for power grid dispatching, which can adapt to market changes, optimize power grid dispatching efficiency and accuracy, and has important practical application value and market prospects.

[0021] The power source in step S1 of this embodiment refers to the power generation equipment in the power grid, and the power generation equipment includes part or all of thermal power plants, hydroelectric power plants, wind power plants and solar power plants. Each power generation equipment has unique physical characteristics and operating parameters, and the load includes part or all of industrial loads, commercial loads, residential loads and agricultural loads 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, operating status, etc., can be recorded. Figure 2 It is a schematic diagram of the power grid topology in an embodiment of the present invention, wherein 1 to 33 are nodes in the power grid, respectively. 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 a source-load model for power sources and loads in step S1 of this embodiment, mathematical formulas are used to express the physical behavior of the source load and the operating laws of the power grid to ensure the accuracy and predictive ability of the model. A physical model is used to obtain the source-load response of the corresponding source load based on the obtained physical characteristics. In this embodiment, the function expression of the source-load model constructed for a 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 a source-load model of a 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.

[0022] In this embodiment, the function expression of the source-load model constructed for the hydroelectric power station is: , in, represents the output power of the hydroelectric power station, and 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 flowing through the turbine. The height of the water drop. Indicates the efficiency of the generator set.

[0023] In this embodiment, the function expression of the physical model constructed for the wind power station is: , in, represents the output power of the wind power station, and 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.

[0024] In this embodiment, 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, and is used as the source-load response of the source-load model of the solar power station; represents the intensity of solar radiation, and Respectively represent the tilt angle and azimuth of the photovoltaic panel, represents the total area of ​​photovoltaic panels, Represents the efficiency of the solar power generator set, represents the temperature coefficient, and Represent the actual temperature and the reference temperature respectively.

[0025] 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: , in, Represents 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. represents fluctuating load, For time.

[0026] 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 of regression analysis is also included to obtain the mathematical relationship between the physical model parameters and the source-load response data in the source-load model, wherein the mathematical relationship is the coefficient of the correlation or the 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.

[0027] like Figure 3 As shown, step S3 of this embodiment 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 elastic coefficients 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 the electricity price changes over time. Any element E in the i-th row and j-th column 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, the weight of the physical model parameters affected by the electricity price change is obtained by multiplying the searched elasticity coefficient by the electricity price change, and the weight of the physical model parameters affected by the electricity price change and the actual physical model parameter data are substituted into the estimated source-load response obtained by the dual-drive source-load model, and the optimal electricity price elasticity correction matrix E is obtained by taking the minimum deviation between the estimated source-load response obtained by the dual-drive source-load model and the actual source-load response as the fitness; 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.

[0028] The historical electricity price data at different times, the actual physical model parameter data, and the actual source-load response in step S3.1 of this embodiment involve long-term monitoring of the power generation equipment and loads in the power grid to collect their historical operation data. Including but not limited to the output power, operating status, maintenance records, fuel consumption of each power generation equipment, and the power demand, power consumption mode, and peak and valley time of each load point. The collection process uses smart meters, automatic data acquisition systems (SCADA), and remote terminal units (RTUs) and other equipment to achieve automated data collection.

[0029] The error index 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 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, root mean square error RMSE, mean absolute error MAE, determination coefficient R² and Nash efficiency coefficient NSE.

[0030] It should be noted that after the current optimal source-load response is sent down in step S5 to control the state 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 increasing training data, adjusting model parameters or adopting a more complex model structure.

[0031] In summary, in the current power system operation, with the deepening of market orientation and the transformation of energy structure, the distribution network faces the dual challenges of increasing uncertainty of source and load and dynamic changes in market factors. The present invention takes the distribution network under this new power market environment as the research object, takes into account the physical characteristics of the power grid and the market operation mechanism, and constructs a source-load model data dual-driven modeling method and system based on market factor correction by comprehensively analyzing the operation data of the power grid, source-load characteristics, power market price signals, and user response behaviors and other multi-dimensional information. This embodiment uses advanced data analysis technology and machine learning algorithms to conduct in-depth mining and intelligent processing of source-load data, and can respond to market changes in real time and dynamically adjust the source-load model. The system can not only provide accurate source-load prediction and scheduling strategies for power grid operators, but also provide decision-making support for market participants and optimize their strategies for participating in the market. Through the implementation of the present invention, power grid operators can respond to market changes more flexibly, achieve optimal allocation and scheduling of source and load, and improve the economy and reliability of power grid operation. This embodiment constructs a highly flexible and adaptable source-load model by comprehensively considering the power system operation data, electricity price signals, and the actual physical characteristics of each source and load. This model can intelligently analyze and predict the source-load changes in the power grid, and provide scientific decision-making support for power grid operators. In particular, the method of this embodiment can dynamically respond to fluctuations in electricity prices, adjust the source-load model through the price elasticity correction matrix, and realize adaptive modeling of each source-load in the power grid. This not only improves the operating efficiency of the power grid, but also enhances the adaptability of the power grid to uncertain factors. In addition, this embodiment also provides a market factor correction mechanism that can quickly adjust the source-load model according to real-time market information to ensure that the power grid dispatching strategy is always consistent with the market environment. This mechanism is particularly suitable for the current rapid changes and highly competitive characteristics of the power market. Through the implementation of this embodiment, power grid operators can more accurately predict and respond to various situations in the operation of the power grid, realize efficient utilization of resources and stable operation of the power grid, and promote the healthy development of the entire power market. This embodiment mainly has the following advantages: 1. Market adaptability: This embodiment can respond to market changes in real time, such as fluctuations in electricity prices and changes in supply and demand relationships, adjust the source-load model through a dynamic correction mechanism, and improve the flexibility and economy of power grid dispatching. 2. Accuracy: By using advanced data analysis and machine learning techniques, this embodiment can more accurately fit the source-load behavior, thereby improving the accuracy and reliability of power grid dispatch. 3. Resource optimization configuration: This embodiment optimizes the configuration of power grid resources through a data dual-driven modeling method, 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, can adapt to power grid systems of different sizes and structures, and has good flexibility and scalability.5. User participation incentives: This embodiment combines the time-of-use electricity price mechanism to provide effective incentives for users to participate in demand response, promote the active participation of user-side resources, and enhance the overall dispatching capacity of the power grid. 6. Economy and efficiency: By optimizing the dispatching strategy, this embodiment helps to reduce the operating costs of the power grid, while improving the efficiency of the power grid operation, achieving a win-win situation of economic and environmental benefits. 7. Technology integration: This embodiment integrates a variety of modeling technologies and market analysis tools to form a comprehensive decision support system, providing a comprehensive solution for power grid operators.

[0032] In addition, this embodiment also provides a dynamically corrected source-load model data dual-driven power 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-driven power grid coordination control method.

[0033] 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 modified source-load model data dual-drive power grid coordination control method through a processor.

[0034] 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.

[0035] 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. 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 codes. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to 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 processes 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 an instruction device, which implements the functions 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, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0036] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A dynamic correction source-load model data dual-drive power grid coordinated control method, characterized in that: The steps include: S1, obtaining the physical characteristic parameters of the power source and load in the power grid and building a source-load model for the power source and load; 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 physical model parameters and electricity price changes and changes; S3, constructing elastic coefficients expressing the influence of electricity price changes for physical model parameters respectively, wherein the elastic coefficients are used to multiply the electricity price changes to obtain weights of the physical model parameters influenced by the electricity price changes, and optimizing the elastic 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 view of the current electricity price changes, the weights of various physical model parameters affected by the electricity price changes are obtained according to the optimal elastic coefficient obtained by 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 supply and load in the power grid.

2. The method for coordinated control of a power grid using a dynamic modified source-load model data dual drive according to claim 1 is characterized in that: The power source in step S1 refers to the power generation equipment in the power grid, which includes part or all of thermal power plants, hydroelectric power plants, wind power plants and solar power plants, and the load includes part or all of industrial loads, commercial loads, residential loads and agricultural loads in the power grid.

3. The method for coordinated control of a power grid using a dynamic modified source-load model data dual drive according to claim 2 is 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 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 a source-load model of a 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, and 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 flowing through the turbine. 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, and 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 functional expression of the source-load model constructed for the solar power station is: ， in, represents the output power of the solar power station, and is used as the source-load response of the source-load model of the solar power station; represents the intensity of solar radiation, and Respectively represent the tilt angle and azimuth of the photovoltaic panel, represents the total area of ​​photovoltaic panels, Represents the efficiency of the solar power generator set, represents the temperature coefficient, and Represent the actual temperature and the reference temperature respectively.

4. The method for coordinated control of a power grid using a dynamic modified source-load model data dual drive according to claim 2 is characterized in that: When constructing source-load models for power sources and loads in step S1, the function expression of the source-load model constructed for the load is: ， in, Represents 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. represents fluctuating load, For time.

5. 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: 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 the various physical model parameters and the source-load response data in the source-load model, wherein the mathematical relationship is a correlation or a coefficient of a fitting polynomial, and the mathematical relationship between the various physical model parameters and the source-load response data is sorted in descending order, 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.

6. 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 elastic coefficients 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 the electricity price changes over time. Any element E in the i-th row and j-th column 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, the weight of the physical model parameters affected by the electricity price change is obtained by multiplying the searched elasticity coefficient by the electricity price change, and the weight of the physical model parameters affected by the electricity price change and the actual physical model parameter data are substituted into the estimated source-load response obtained by the dual-drive source-load model, and the optimal electricity price elasticity correction matrix E is obtained by taking the minimum deviation between the estimated source-load response obtained by the dual-drive source-load model and the actual source-load response as the fitness; 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.

7. The method for coordinated control of a power grid using a dynamic modified source-load model data dual drive according to claim 6 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 mean square error MSE, root mean square error RMSE, mean absolute error MAE, determination coefficient R² and Nash efficiency coefficient NSE.

8. A dynamic correction type 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 correction type source-load model data dual-drive power grid coordination control method as described in any one of claims 1 to 7.

9. 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 coordination control method described in any one of claims 1 to 7 through a processor.

10. 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 coordination control method described in any one of claims 1 to 7 through a processor.

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