Control method and system for relieving power transmission congestion by considering demand response of wind power resources

Abstract

This invention belongs to the field of power systems, specifically disclosing a control method and system for alleviating transmission congestion by considering wind power resource demand response. Based on data from wind turbine generators and user participation in demand response, a transmission congestion management model is established. An objective function and constraints for minimizing the total cost of transmission congestion management are constructed and solved to obtain the minimum total cost. Based on this minimum cost, wind turbine generators are controlled using power demand response, enabling optimal planning of distributed energy resources, reducing network congestion, and allowing end-users to participate in transmission congestion management through demand response. This alleviates transmission congestion without increasing fuel costs, reduces transmission line overload, optimizes power flow, and effectively reduces system operating costs. It enables more rational management of power system transmission congestion, maximizing system resource utilization and improving economic efficiency without increasing system operating costs.

Description

Technical Field

[0001] This invention belongs to the field of power systems, and specifically relates to a control method and system for alleviating transmission congestion by taking into account the demand response of wind power resources. Background Technology

[0002] The elements of a competitive electricity market include market participants, namely sellers and buyers; and market objects, namely the items traded between the buyers and sellers, such as electricity, transmission rights, and ancillary services. In the current competitive market environment, insufficient actual transmission capacity of the power grid will hinder electricity market transactions.

[0003] To prevent transmission system overload from interfering with electricity market plans or contractual transactions, transmission congestion management is necessary. Currently, management models are mainly divided into market-based and non-market-based approaches. However, the increasing demand for electricity and the growing availability of renewable energy have exacerbated the congestion problem, making it difficult to solve simply through first-come, first-served, proportional capacity allocation. In congested networks, distributed energy resources can provide power at bottleneck locations, reducing transmission system congestion. Simultaneously, reducing or shifting demand from peak hours to off-peak hours can reduce congestion. Therefore, active participation of end-user consumers in demand response can more effectively eliminate congestion.

[0004] However, in practical applications, improper planning of distributed energy resources can lead to increased network congestion, failure to fully utilize market mitigation mechanisms, difficulty for end users to participate in transmission congestion management through demand response, transmission line overload, and increased system operating costs. Summary of the Invention

[0005] To address the aforementioned issues, this invention proposes a control method and system for mitigating transmission congestion by considering demand response to wind power resources. This method optimizes distributed energy planning, reduces network congestion, and fully utilizes market mitigation mechanisms to enable end-users to participate in transmission congestion management through demand response. This alleviates transmission congestion, reduces transmission line overload, optimizes power system flow, and effectively reduces system operating costs.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a control method for alleviating transmission congestion by taking into account the demand response of wind power resources, including:

[0008] Acquire data from wind turbine generators, user participation in demand response, and thermal power units;

[0009] A transmission congestion management model is established based on data from wind turbine units and user participation in demand response.

[0010] Based on the transmission congestion management model and data from thermal power units, an objective function and constraints for minimizing the total cost of transmission congestion management are constructed.

[0011] Based on the aforementioned constraints, the objective function is solved to obtain the minimum total cost of transmission congestion management.

[0012] Control of wind turbines based on power demand response, based on minimizing the total cost of transmission congestion management.

[0013] Furthermore, the establishment of a transmission congestion management model based on wind turbine data and user-participated demand response data specifically includes:

[0014] The transmission congestion management model includes a mathematical model of wind turbine output and a mathematical model of demand response behavior;

[0015] The mathematical model for the output of the wind turbine is expressed as follows:

[0016]

[0017] Among them, P wind ρ is the output power of the wind turbine. wind V is the air density factor, A is the swept area of ​​the wind turbine rotor, γ is the overall efficiency of wind power generation, and V is the air density factor. wind The wind speed at a given altitude;

[0018] The mathematical model for the demand response behavior is expressed as follows:

[0019]

[0020] Among them, D m For linear response load modes, D 0m To respond to the power demand on the front bus m, ω is the load elasticity, σ 0 σ is the electricity price before the response, INC is the incentive coefficient, and PEN is the penalty coefficient.

[0021] Furthermore, based on the transmission congestion management model and data from thermal power units, the objective function for minimizing the total cost of transmission congestion management is constructed, specifically including:

[0022] The data for the wind turbine units includes the cost of wind power generation; the data for user participation in demand response includes the cost of demand response; and the data for the thermal power units includes the cost of thermal power generation.

[0023] The objective function for minimizing the total cost of transmission congestion management is expressed as follows:

[0024]

[0025] Where C represents the total cost of transmission congestion management based on demand response. INC represents the cost of thermal power generation, where n is the number of thermal power generating units. m N is the total stimulus given to the user on the m-th response bus. DR For the number of demand response buses, The wind turbine generator has an output power of P. wind Cost of time.

[0026] Furthermore, the constraints include: power balance constraints, demand response cost constraints, transmission line constraints, and generator set power constraints;

[0027] The expression for the power balance constraint is:

[0028]

[0029] Among them, P i Let i be the active power consumed at node i. This represents the active power of the thermal power generating unit at node i. Q represents the active power of the wind turbine generator at node i; i Let i be the reactive power consumed at node i. The reactive power of the thermal power generating unit at node i. N represents the reactive power of the wind turbine generator at node i. i The number of nodes;

[0030] The expression for the demand response cost constraint is:

[0031]

[0032] Among them, INC m The total stimulus given to the user on the m-th response bus. and These are the minimum and maximum limits of the demand response stimulus on the m-th response bus, respectively;

[0033] The expressions for the transmission line constraints and generator power constraints are as follows:

[0034]

[0035] Among them, V i For the power transmitted by transmission line i, V i Min and V i Max Let N be the minimum and maximum values ​​of the power transmitted by transmission line i, respectively. i The total number of transmission lines, The active power output of thermal power generating unit i. and These represent the minimum and maximum values ​​of the active power output of thermal power generating unit i, respectively. The reactive power output of thermal power generating unit i is... and Let P be the minimum and maximum reactive power output of thermal power generating unit i, respectively. wind This refers to the output power of the wind turbine generator set. This represents the maximum output power of the wind turbine generator set.

[0036] Furthermore, it also includes: constructing a fitness function based on the objective function of minimizing the total cost of transmission congestion management, specifically including,

[0037] The fitness function includes wind power generation cost, thermal power generation cost, demand response cost, and total transmission congestion management cost.

[0038] Furthermore, the process of solving the objective function based on the aforementioned constraints specifically includes using a genetic algorithm:

[0039] Create an initial random population, calculate the fitness function value of each individual in the population, and then perform selection, crossover, and mutation on the initial random population to form a progeny population. Calculate the fitness function value of each individual in the progeny population.

[0040] Furthermore, it also includes: calculating the minimum total transmission congestion management cost of the objective function corresponding to the optimal output fitness function through iterative calculation;

[0041] When the fitness function converges or the number of iterations reaches a preset value, the fitness function reaches its optimum, the corresponding objective function produces the optimal solution, and the minimum total cost of transmission congestion management is obtained.

[0042] Secondly, the present invention provides a control system for alleviating transmission congestion by considering the demand response of wind power resources, comprising:

[0043] The acquisition module is used to acquire data from wind turbine generator sets, user participation in demand response, and thermal power units.

[0044] The transmission congestion management model establishment module is used to establish a transmission congestion management model based on data from wind turbine units and user participation demand response data.

[0045] The objective function and constraint construction module is used to construct the objective function and constraints that minimize the total cost of transmission congestion management based on the transmission congestion management model and the data of thermal power units.

[0046] The solution module is used to solve the objective function based on the constraints to obtain the minimum total cost of transmission congestion management.

[0047] The control module is used to control the power demand response-based wind turbine based on the minimum total cost of transmission congestion management.

[0048] Thirdly, the present invention provides a computer device, comprising,

[0049] A memory for storing a computer program; a processor for executing the computer program to implement the control method for alleviating transmission congestion by taking into account wind power resources as described in any one of the above descriptions.

[0050] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the control method for alleviating transmission congestion by taking into account wind power resources as described in any one of the above descriptions.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0052] This invention proposes a control method for mitigating transmission congestion by considering demand response to wind power resources. Based on wind turbine data and user participation in demand response, a transmission congestion management model is established. An objective function and constraints for minimizing the total cost of transmission congestion management are constructed and solved using a genetic algorithm to obtain the minimum total cost. Based on this minimum cost, wind turbines are controlled using demand response, enabling optimal planning of distributed energy resources, reducing network congestion, and fully utilizing market mitigation mechanisms to allow end-users to participate in transmission congestion management through demand response. This alleviates transmission congestion, reduces transmission line overload, optimizes power flow, and effectively lowers system operating costs.

[0053] Furthermore, when constructing the objective function, the selection of the objective function needs to consider the cost of wind power generation and the cost of demand response. At the same time, since there are coal consumption costs for thermal power generation in the system, the cost of thermal power generation should also be considered in the objective function, so that the final objective function is more in line with the actual operating costs of the power system in production and life.

[0054] This invention proposes a decision system that considers demand response to wind power resources to alleviate transmission congestion. It constructs an objective function that considers the costs of thermal power generation, demand response, and wind power generation. The objective function is solved using an artificial intelligence-based genetic algorithm to obtain the optimal layout of wind turbine generators based on power demand response. This optimizes the power system, alleviating transmission congestion and reducing transmission line overload without increasing fuel costs. It enables more rational management of power system transmission congestion, maximizing system resource utilization and improving economic efficiency without increasing system operating costs. Attached Figure Description

[0055] Figure 1 This is a flowchart of the control method and system for alleviating transmission congestion by considering the demand response of wind power resources, as proposed in this invention.

[0056] Figure 2 The system module diagram of the control method and system for alleviating transmission congestion by considering the demand response of wind power resources proposed in this invention is shown. Detailed Implementation

[0057] This section will describe in detail specific embodiments of the present invention. Preferred embodiments of the present invention are shown in the accompanying drawings. The purpose of the drawings is to supplement the textual description with graphics, so that people can intuitively and vividly understand each technical feature and overall technical solution of the present invention, but they should not be construed as limiting the scope of protection of the present invention.

[0058] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0059] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0060] This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, elements, data structures, etc., that perform a specific task or implement a specific abstract data type. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0061] In this invention, terms such as "module," "device," and "system" refer to relevant entities applied to a computer, such as hardware, combinations of hardware and software, software, or software in execution. More specifically, for example, an element can be, but is not limited to, a process running on a processor, a processor, an object, an executable element, an execution thread, a program, and / or a computer. Furthermore, an application program or script running on a server, and the server itself, can also be an element. One or more elements may be in an execution process and / or thread, and elements may be localized on a single computer and / or distributed across two or more computers, and may be run on various computer-readable media. Elements can also communicate via local and / or remote processes based on signals having one or more data packets, for example, signals from data interacting with another element in a local system, a distributed system, and / or interacting with other systems via signals over a network of the Internet.

[0062] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising" or "including" include not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0063] The present invention will be further described in detail below with reference to specific embodiments. These descriptions are for explanation purposes only and are not intended to limit the scope of the invention.

[0064] Example 1

[0065] This invention proposes a control method for alleviating transmission congestion by considering the demand response of wind power resources. Please refer to [link / reference]. Figure 1 ,include,

[0066] Acquire data from wind turbine generators, user participation in demand response, and thermal power units;

[0067] A transmission congestion management model is established based on data from wind turbine units and user participation in demand response.

[0068] Based on the transmission congestion management model and data from thermal power units, an objective function and constraints for minimizing the total cost of transmission congestion management are constructed.

[0069] Based on the aforementioned constraints, the objective function is solved to obtain the minimum total cost of transmission congestion management.

[0070] Control of wind turbines based on power demand response, based on minimizing the total cost of transmission congestion management.

[0071] Based on data from wind turbine generators and user participation in demand response, a transmission congestion management model is established. An objective function and constraints for minimizing the total cost of transmission congestion management are constructed and solved using a genetic algorithm. The minimum total cost of transmission congestion management is obtained. Based on this minimum cost, wind turbine generators are strategically deployed using demand response, and distributed energy resources are optimally planned to reduce network congestion. By fully utilizing market mitigation mechanisms, end-users can participate in transmission congestion management through demand response, alleviating transmission congestion, reducing transmission line overload, optimizing power flow, and effectively lowering system operating costs.

[0072] In a specific embodiment of the present invention, please refer to Figure 1 The establishment of a transmission congestion management model based on wind turbine data and user-participated demand response data specifically includes:

[0073] The transmission congestion management model includes a mathematical model of wind turbine output and a mathematical model of demand response behavior;

[0074] The mathematical model for the output of the wind turbine is expressed as follows:

[0075]

[0076] Among them, P wind ρ is the output power of the wind turbine. wind V is the air density factor, A is the swept area of ​​the wind turbine rotor, γ is the overall efficiency of wind power generation, and V is the air density factor. wind The wind speed at a given altitude;

[0077] The mathematical model for the demand response behavior is expressed as follows:

[0078]

[0079] Among them, D m For linear response load modes, D 0m To respond to the power demand on the front bus m, ω is the load elasticity, σ 0 σ is the electricity price before the response, INC is the incentive coefficient, and PEN is the penalty coefficient.

[0080] The calculation process of the mathematical model for the demand response behavior is as follows:

[0081] Demand adjustment amount on the m-th response bus:

[0082] ΔD m =D0m -D m ;

[0083] Where, ΔD m For the demand adjustment status on the m-th response bus, D 0m In response to the power demand on the front bus m, D m In response to the power demand on the back bus m;

[0084] The total stimulus given to the user on the m-th response bus:

[0085] INC m =INC[D 0m -D m ];

[0086] Where, ΔD m INC represents the total incentive given to the user on the m-th response bus, where INC is the incentive coefficient, ranging from 0.1 to 10 times the electricity price.

[0087] Users participating in DR must reduce their load to the minimum required level. The total penalty for users not participating in the response is as follows:

[0088] PEN m =PEN[LR m -ΔD m ];

[0089] Among them, PEN m PEN represents the total penalty imposed on the user on the m-th response bus, where PEN is the penalty coefficient, ranging from 0.1 to 10 times the electricity price, and LR is the total penalty imposed on the user on the m-th response bus. m This represents the minimum load reduction required by the system on the m-th response bus.

[0090] Linear response load modes:

[0091]

[0092] Where ω is the load elasticity, σ 0 σ and σ represent the electricity prices before and after the response, respectively.

[0093] In a specific embodiment of the present invention, please refer to Figure 1 The objective function and constraints for minimizing the total cost of transmission congestion management are constructed based on the transmission congestion management model and data from thermal power units. Specifically, this includes:

[0094] The data for the wind turbine units includes the cost of wind power generation; the data for user participation in demand response includes the cost of demand response; and the data for the thermal power units includes the cost of thermal power generation.

[0095] The objective function for minimizing the total cost of transmission congestion management is expressed as follows:

[0096]

[0097] Where C represents the total cost of transmission congestion management based on demand response. INC represents the cost of thermal power generation, where n is the number of thermal power generating units. m N is the total stimulus given to the user on the m-th response bus. DR For the number of demand response buses, The wind turbine generator has an output power of P. wind Cost of time.

[0098] It also includes a definition of the cost of thermal power generation:

[0099]

[0100] Among them, P Gj For the output power of thermal power generating unit j, These are the cost coefficients for thermal power generation, and n is the number of thermal power generating units.

[0101] Wind power generation requires collaborative support from both the generation and user sides. On the generation side, the uncertainty of wind resources makes wind power generation costly. On the user side, reasonable demand response can guide customers to stagger their electricity consumption during off-peak hours and optimize load structure. Therefore, the participation of wind power and demand response can regulate the operating costs of the power system.

[0102] Therefore, when constructing the objective function to minimize the total cost of transmission congestion management, it is necessary to consider the cost of wind power generation and demand response costs. At the same time, because when congestion occurs, the load increment of some lines may not be produced by the cheapest wind turbines and thermal power units are needed as a supplement. In addition, if wind energy resources are insufficient, thermal power is required to supply energy. Therefore, there are coal consumption costs for thermal power generation in the system. Hence, the cost of thermal power generation is also considered in the objective function, so that the final objective function is more in line with the actual operating costs of the power system in production and life.

[0103] In a specific embodiment of the present invention, please refer to Figure 1 The constraints include: power balance constraints, demand response cost constraints, transmission line constraints, and generator set power constraints.

[0104] The expression for the power balance constraint is:

[0105]

[0106] Among them, P i Let i be the active power consumed at node i. This represents the active power of the thermal power generating unit at node i. Q represents the active power of the wind turbine generator at node i; i Let i be the reactive power consumed at node i. The reactive power of the thermal power generating unit at node i. N represents the reactive power of the wind turbine generator at node i. i The number of nodes;

[0107] The expression for the demand response cost constraint is:

[0108]

[0109] Among them, INC m The total stimulus given to the user on the m-th response bus. and These are the minimum and maximum limits of the demand response stimulus on the m-th response bus, respectively;

[0110] The expressions for the transmission line constraints and generator power constraints are as follows:

[0111]

[0112] Among them, V i For the power transmitted by transmission line i, V i Min and V i Max Let N be the minimum and maximum values ​​of the power transmitted by transmission line i, respectively. i The total number of transmission lines, The active power output of thermal power generating unit i. and These represent the minimum and maximum values ​​of the active power output of thermal power generating unit i, respectively. The reactive power output of thermal power generating unit i is... and Let P be the minimum and maximum reactive power output of thermal power generating unit i, respectively. wind This refers to the output power of the wind turbine generator set. This represents the maximum output power of the wind turbine generator set.

[0113] In a specific embodiment of the present invention, please refer to Figure 1 It also includes:

[0114] Construct a fitness function based on the objective function of minimizing the total cost of transmission congestion management;

[0115] The fitness function includes wind power generation cost, thermal power generation cost, demand response cost, and total transmission congestion management cost.

[0116] The fitness function, also called the evaluation function, mainly determines the fitness of an individual based on its characteristics. In this invention, the optimal solution is obtained by determining the fitness function that minimizes the overall cost. Genetic algorithms, in evolutionary search, largely do not utilize external information; they rely solely on the fitness function and the fitness value of each individual in the population to perform the search. A mapping relationship is established between the objective function that minimizes the total cost of transmission congestion management and the fitness value of each individual, thereby optimizing the objective function that minimizes the total cost of transmission congestion management during the population evolution process.

[0117] In a specific embodiment of the present invention, please refer to Figure 1 The process of solving the objective function based on the aforementioned constraints specifically includes using a genetic algorithm.

[0118] Create an initial random population, calculate the fitness function value of each individual in the population, and then perform selection, crossover, and mutation on the initial random population to form a progeny population. Calculate the fitness function value of each individual in the progeny population.

[0119] The initial random population is set to 200. The fitness function value corresponding to each individual in the population, i.e. each solution, is found. Then, repeated selection, crossover and mutation are performed to generate the required next generation. Based on the newly formed next generation, the fitness function is calculated again to obtain the optimal layout of wind turbine generators based on power demand response.

[0120] In a specific embodiment of the present invention, please refer to Figure 1 It also includes:

[0121] The minimum total cost of transmission congestion management is obtained by iteratively calculating the objective function corresponding to the optimal output fitness function.

[0122] When the fitness function converges or the number of iterations reaches a preset value, the fitness function reaches its optimum, and the corresponding objective function produces the optimal solution, which is the minimum total cost of transmission congestion management.

[0123] Because genetic algorithms are approximate optimization algorithms, each of their solutions is approximately optimal, which means that the results of each generation of offspring may differ. However, when the fitness function converges to a value and tends to plateau, or when the preset number of iterations can be exceeded, this local optimum can be taken as the global optimum and output as the optimal solution. Moreover, there is only one optimal solution.

[0124] Example 2

[0125] This invention proposes a control system that considers the demand response of wind power resources to alleviate transmission congestion. Please refer to [link / reference]. Figure 2 ,include,

[0126] The acquisition module is used to acquire data from wind turbine generator sets, user participation in demand response, and thermal power units.

[0127] The transmission congestion management model establishment module is used to establish a transmission congestion management model based on data from wind turbine units and user participation demand response data.

[0128] The objective function and constraint construction module is used to construct the objective function and constraints that minimize the total cost of transmission congestion management based on the transmission congestion management model and the data of thermal power units.

[0129] The solution module is used to solve the objective function based on the constraints to obtain the minimum total cost of transmission congestion management.

[0130] The control module is used to control the power demand response-based wind turbine based on the minimum total cost of transmission congestion management.

[0131] An objective function considering the costs of thermal power generation, demand response, and wind power generation is constructed. This objective function is then solved using an AI-based genetic algorithm to obtain the optimal layout of wind turbine generators based on electricity demand response. This optimizes the power system, alleviating transmission congestion and reducing transmission line overload without increasing fuel costs. This allows for more rational management of power system transmission congestion, maximizing system resource utilization and improving economic efficiency without increasing system operating costs.

[0132] Example 3

[0133] This invention proposes a computer device, comprising,

[0134] A memory for storing computer programs; a processor for executing the computer programs to implement the control method for alleviating transmission congestion by considering wind power resource demand response as described in Embodiment 1.

[0135] Example 4

[0136] This invention proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the control method for alleviating transmission congestion by considering wind power resource demand response as described in Embodiment 1.

[0137] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0138] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0139] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0140] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A control method for mitigating power transmission congestion considering demand response of wind power resources, characterized by, include: Acquire data from wind turbine generators, user participation in demand response, and thermal power units; A transmission congestion management model is established based on data from wind turbine units and user participation in demand response. Based on the transmission congestion management model and data from thermal power units, an objective function and constraints for minimizing the total cost of transmission congestion management are constructed. Based on the aforementioned constraints, the objective function is solved to obtain the minimum total cost of transmission congestion management. Control of wind turbines based on power demand response, based on the minimum total cost of transmission congestion management; The establishment of a transmission congestion management model based on wind turbine data and user-participated demand response data specifically includes: The transmission congestion management model includes a mathematical model of wind turbine output and a mathematical model of demand response behavior; The mathematical model for the output of the wind turbine is expressed as follows: ； in, This refers to the output power of the wind turbine. Air density factor, The swept area of ​​the wind turbine rotor. For the overall efficiency of wind power generation The wind speed at a given altitude; The mathematical model for the demand response behavior is expressed as follows: ； in, For linear response load modes, In response to the power demand on the front bus m, For load elasticity, The electricity price before the response, The electricity price after the response, For the incentive coefficient, This is the penalty coefficient; The constraints include: power balance constraints, demand response cost constraints, transmission line constraints, and generator set power constraints. The expression for the power balance constraint is: ； in, Let be the active power consumed at node i. This represents the active power of the thermal power generating unit at node i. This represents the active power of the wind turbine generator at node i. Let i be the reactive power consumed at node i. The reactive power of the thermal power generating unit at node i. Let be the reactive power of the wind turbine generator at node i. The number of nodes; The expression for the demand response cost constraint is: ； wherein, is the total incentive given to the user on the mth response bus, and are the minimum and maximum limits of the demand response incentive on the mth response bus, respectively. The expressions for the transmission line constraints and generator power constraints are as follows: ； in, For power transmission line i, and These are the minimum and maximum values ​​of the power transmitted by transmission line i, respectively. The total number of transmission lines, The active power output of thermal power generating unit i. and These represent the minimum and maximum values ​​of the active power output of thermal power generating unit i, respectively. The reactive power output of thermal power generating unit i is... and These represent the minimum and maximum reactive power output of thermal power generating unit i, respectively. This refers to the output power of the wind turbine generator set. This represents the maximum output power of the wind turbine generator set.

2. The control method for alleviating transmission congestion by considering the demand response of wind power resources according to claim 1, characterized in that, The objective function for minimizing the total cost of transmission congestion management is constructed based on the transmission congestion management model and data from thermal power units, specifically including: The data for the wind turbine units includes the cost of wind power generation; the data for user participation in demand response includes the cost of demand response; and the data for the thermal power units includes the cost of thermal power generation. The objective function for minimizing the total cost of transmission congestion management is expressed as follows: ； in, The total cost of demand-response-based transmission congestion management, For the cost of thermal power generation, This refers to the number of thermal power generating units. The total stimulus given to the user on the m-th response bus. For the number of demand response buses, The output power of the wind turbine generator is Cost of time.

3. The method of claim 1, wherein the method further comprises: Also includes: The fitness function is constructed based on the objective function of minimizing the total cost of transmission congestion management, specifically including: The fitness function includes wind power generation cost, thermal power generation cost, demand response cost, and total transmission congestion management cost.

4. The control method for demand response mitigation of power transmission congestion considering wind power resources according to claim 3, wherein, Solving the objective function based on the aforementioned constraints specifically includes using a genetic algorithm: Create an initial random population, calculate the fitness function value of each individual in the population, and then perform selection, crossover, and mutation on the initial random population to form a progeny population. Calculate the fitness function value of each individual in the progeny population.

5. The method of claim 4, wherein, Also includes: The minimum total cost of transmission congestion management is obtained by iteratively calculating the objective function corresponding to the optimal output fitness function. When the fitness function converges or the number of iterations reaches a preset value, the fitness function reaches its optimum, the corresponding objective function produces the optimal solution, and the minimum total cost of transmission congestion management is obtained.

6. A control system for mitigating power transmission congestion considering demand response of wind power resources for implementing the control method for mitigating power transmission congestion considering demand response of wind power resources according to claim 1, characterized in that, include: The acquisition module is used to acquire data from wind turbine generator sets, user participation in demand response, and thermal power units. The transmission congestion management model establishment module is used to establish a transmission congestion management model based on data from wind turbine units and user participation demand response data. The objective function and constraint construction module is used to construct the objective function and constraints that minimize the total cost of transmission congestion management based on the transmission congestion management model and the data of thermal power units. The solution module is used to solve the objective function based on the constraints to obtain the minimum total cost of transmission congestion management. The control module is used to control the power demand response-based wind turbine based on the minimum total cost of transmission congestion management.

7. A computer device, characterized by include: Memory, used to store computer programs; A processor, configured to implement, when executing the computer program, the control method for mitigating transmission congestion by taking into account wind power resources as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the control method for alleviating transmission congestion by taking into account wind power resources as described in any one of claims 1 to 5.

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