Capacity market VRR curve adaptive optimization method and system

Abstract

The invention discloses a capacity market VRR curve adaptive optimization method and system, and the method comprises the steps: firstly obtaining system operation parameters, and then constructing a VRR curve adaptive optimization objective function according to the obtained operation parameters; thirdly, according to the obtained operation parameters, conventional operation constraints of the system are constructed; then, according to the obtained operation parameters, constructing VRR curve correlation constraints considering multi-dimensional evaluation; and finally, according to the obtained operation parameters, constructing a dynamic adjustment model of the VRR curve. According to the method, a comprehensive evaluation system including system flexibility, frequency modulation capability and capacity reliability is established by starting with capacity market mechanism design, taking system operation efficiency optimization as a target and considering multi-dimensional performance characteristics of a unit, and uncertainty influence caused by large-scale access of new energy is effectively dealt with through self-adaptive optimization design of a VRR curve, so that the reliability of the system is improved. The system resource configuration efficiency is improved, and accurate conduction of capacity market price signals is realized.

Description

Technical Field

[0001] This invention relates to the field of power market and power system optimization operation technology, and in particular to an adaptive optimization method and system for capacity market VRR curves. Background Technology

[0002] As the proportion of new energy sources in the power system continues to increase, their intermittency, volatility, and uncertainty pose significant challenges to the reliable operation of the power system. Traditional capacity market VRR curve design mainly focuses on the system's capacity adequacy, failing to fully consider the frequency regulation response capability of resources and the system's flexibility requirements.

[0003] From a system reliability perspective, the large-scale integration of new energy sources has significantly increased the demand for frequency regulation response capabilities. Traditional VRR curve design cannot accurately reflect the differences in frequency regulation performance among different types of generating units, resulting in a lack of reasonable price incentives for high-quality frequency regulation resources. At the same time, insufficient system flexibility may limit the absorption of new energy sources, affecting the overall operating efficiency of the system.

[0004] From a market mechanism perspective, existing VRR curves, designed using a static approach, are ill-suited to adapt to dynamic changes in system operating conditions. When a system faces a high proportion of renewable energy penetration, static VRR curves fail to reflect the system's differentiated needs for frequency regulation capabilities and flexibility, impacting the efficiency of resource allocation in the capacity market. Furthermore, a single capacity assessment dimension cannot accurately reflect the comprehensive value of different types of resources.

[0005] Therefore, it is necessary to conduct research on the optimization of VRR curves in capacity markets that considers multidimensional evaluation. Summary of the Invention

[0006] Therefore, the purpose of this application is to propose an adaptive optimization method for the VRR curve in the capacity market. By introducing frequency regulation response capability and system flexibility assessment, a dynamic adaptive VRR curve design method is established to improve the resource allocation efficiency of the capacity market, enhance the frequency regulation capability and flexibility of the system, and provide strong support for the large-scale consumption of new energy.

[0007] Another objective of this invention is to propose an adaptive optimization system for capacity market VRR curves.

[0008] The third objective of this invention is to provide a computer device.

[0009] A fourth objective of this invention is to provide a non-transitory computer-readable storage medium.

[0010] To achieve the above objectives, a first aspect of the present invention proposes an adaptive optimization method for capacity market VRR curves, comprising: Obtain system operating parameters, including unit frequency regulation characteristics, system flexibility indicators, and capacity reliability indicators; Based on the system operating parameters, construct the objective function for adaptive optimization of the VRR curve, the system's normal operating constraints, and the VRR curve-related constraints for multidimensional evaluation. A dynamic adjustment model is constructed based on the objective function, system operating constraints, and VRR curve-related constraints, and the optimized VRR curve parameters are obtained by solving the model.

[0011] In one embodiment of the present invention, constructing an objective function for adaptive optimization of the VRR curve includes: (1) in, , They represent the generating units. exist The price and quantity of capacity for each time period; Indicates the unit exist Frequency modulation response capability indicators for different time periods; Indicates the unit exist System flexibility indicators for different time periods; , , These are the penalty coefficients.

[0012] In one embodiment of the present invention, constructing system routine operation constraints includes: Capacity adequacy constraints: (2) Unit capacity upper and lower limit constraints: (3) Unit availability constraints: (4) VRR curve slope constraint: (5) Price upper and lower limits constraints: (6) Frequency modulation response capability constraints: (7) System flexibility constraints: (8) in, Indicates the unit exist Capacity during a given time period; express System capacity requirements for a given time period; , They represent the generating units. Minimum and maximum capacity limits; Indicates the unit The installed capacity; Indicates the unit The equivalent forced shutdown rate; Indicates the unit exist Capacity pricing for specific time periods; , These represent the minimum and maximum allowable slopes of the VRR curve, respectively; , These represent the lower and upper limits of the capacity price, respectively. Indicates the unit exist Frequency modulation response capability indicators for different time periods; This indicates the minimum frequency regulation response capability required by unit i; Indicates the unit exist System flexibility indicators for different time periods; Indicates the unit The minimum required level of system flexibility.

[0013] In one embodiment of the present invention, constructing VRR curve-related constraints for multidimensional evaluation includes: Frequency modulation response capability evaluation constraints: (9) System flexibility assessment constraints: (10) VRR curve characteristic constraints: (11) in: Indicates the unit exist Frequency modulation response time for a given period; Indicates the maximum allowed response time; Indicates the unit exist Frequency adjustment deviation during the time period; Indicates the maximum permissible frequency deviation; Indicates the unit exist The duration of frequency modulation during the time period; Indicates the minimum required duration; Indicates the unit exist Adjustable capacity for different time periods; This represents the minimum adjustable capacity proportional coefficient; Indicates the unit Maximum climbing rate; Indicates the unit exist The duration of the time slot can be flexibly adjusted. Indicates the minimum required flexibility duration; Indicates the maximum allowed price change; Indicates the maximum permissible slope variation; This indicates net cost.

[0014] In one embodiment of the present invention, constructing a dynamic adjustment model for the VRR curve includes: (12) in: Indicates the unit exist Market settlement amount for the specified time period; Indicates the unit exist The assessment amount for frequency modulation response during specific time periods; Indicates the unit exist The amount allocated to assess system flexibility during specific time periods; Indicates the unit exist The amount assessed based on price fluctuations over a given period.

[0015] To achieve the above objectives, a second aspect of the present invention provides a capacity market VRR curve adaptive optimization system, comprising: The parameter acquisition module is used to acquire system operating parameters, including unit frequency regulation characteristics, system flexibility indicators, and capacity reliability indicators. The constraint construction module is used to construct, based on the system operating parameters, the objective function for adaptive optimization of the VRR curve, the system's normal operating constraints, and the VRR curve-related constraints for multidimensional evaluation. The model solving module is used to construct a dynamically adjusted model based on the objective function, system operating constraints, and VRR curve-related constraints, and solve for the optimized VRR curve parameters.

[0016] The capacity market VRR curve adaptive optimization method and system of this invention starts from the design of the capacity market mechanism, aims at optimizing system operating efficiency, considers the multi-dimensional performance characteristics of the generating units, and establishes a comprehensive evaluation system that includes system flexibility, frequency regulation capability and capacity reliability. Through the adaptive optimization design of the VRR curve, it effectively copes with the uncertainty brought about by the large-scale access of new energy sources, improves the efficiency of system resource allocation, and realizes the accurate transmission of capacity market price signals.

[0017] To achieve the above objectives, a third aspect of this application provides a computer device comprising: a processor and a memory; wherein the processor runs a program corresponding to the executable program code stored in the memory, for implementing the capacity market VRR curve adaptive optimization method as described in the first aspect embodiment.

[0018] To achieve the above objectives, a fourth aspect of this application provides a non-transitory computer-readable storage medium having a computer program stored thereon that, when executed by a processor, implements the capacity market VRR curve adaptive optimization method as described in the first aspect embodiment.

[0019] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0020] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart of an adaptive optimization method for capacity market VRR curves provided in an embodiment of the present invention; Figure 2 A structural diagram of a capacity market VRR curve adaptive optimization system provided in an embodiment of the present invention; Figure 3 The computer device provided in the embodiments of the present invention. Detailed Implementation

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] The following describes, with reference to the accompanying drawings, an adaptive optimization method and system for capacity market VRR curves according to embodiments of the present invention.

[0024] This embodiment provides an adaptive optimization method for capacity market VRR curves. For example... Figure 1 As shown, it includes: S1, Obtain system operating parameters, wherein the system operating parameters include unit frequency regulation characteristics, system flexibility indicators and capacity reliability indicators; S2, construct the objective function for adaptive optimization of VRR curve, the system's normal operation constraints, and the VRR curve-related constraints for multidimensional evaluation based on the system's operating parameters; S3. A dynamic adjustment model is constructed based on the objective function, system operating constraints, and VRR curve-related constraints, and the optimized VRR curve parameters are obtained by solving the model.

[0025] Specifically, this invention starts with the design of a capacity market mechanism, aiming at optimizing system operating efficiency. It achieves dynamic optimization of the VRR curve by establishing a multi-dimensional evaluation system that includes frequency regulation response capability and system flexibility. This method fully considers the performance characteristics of different types of generating units, establishes differentiated evaluation indicators, and enables the VRR curve to accurately reflect the comprehensive value of resources. This effectively addresses the uncertainties brought about by the integration of new energy sources, improving system resource allocation efficiency and power supply reliability.

[0026] This embodiment acquires system operating parameters, including unit frequency regulation characteristics, system flexibility indicators, and capacity reliability indicators; based on the acquired operating parameters, it constructs an objective function for adaptive optimization of the VRR curve; based on the acquired operating parameters, it constructs system conventional operating constraints; based on the acquired operating parameters, it constructs VRR curve-related constraints considering multi-dimensional evaluation; finally, based on the acquired data, it constructs a dynamic adjustment model for the VRR curve and solves to obtain the optimized VRR curve parameters.

[0027] Furthermore, the construction of the objective function for adaptive optimization of the VRR curve includes: (1) in, , They represent the generating units. exist The price and quantity of capacity for each time period; Indicates the unit exist Frequency modulation response capability indicators for different time periods; Indicates the unit exist System flexibility indicators for different time periods; , , These are the penalty coefficients.

[0028] Furthermore, the construction of the system's routine operational constraints includes: Capacity adequacy constraints: (2) Unit capacity upper and lower limit constraints: (3) Unit availability constraints: (4) VRR curve slope constraint: (5) Price upper and lower limits constraints: (6) Frequency modulation response capability constraints: (7) System flexibility constraints: (8) in, Indicates the unit exist Capacity during a given time period; express System capacity requirements for a given time period; , They represent the generating units. Minimum and maximum capacity limits; Indicates the unit The installed capacity; Indicates the unit The equivalent forced shutdown rate; Indicates the unit exist Capacity pricing for specific time periods; , These represent the minimum and maximum allowable slopes of the VRR curve, respectively; , These represent the lower and upper limits of the capacity price, respectively. Indicates the unit exist Frequency modulation response capability indicators for different time periods; This indicates the minimum frequency regulation response capability required by unit i; Indicates the unit exist System flexibility indicators for different time periods; Indicates the unit The minimum required level of system flexibility.

[0029] Furthermore, the construction of constraints related to the VRR curve in the multidimensional evaluation is considered, including: Frequency modulation response capability evaluation constraints: (9) System flexibility assessment constraints: (10) VRR curve characteristic constraints: (11) in: Indicates the unit exist Frequency modulation response time for a given period; Indicates the maximum allowed response time; Indicates the unit exist Frequency adjustment deviation during the time period; Indicates the maximum permissible frequency deviation; Indicates the unit exist The duration of frequency modulation during the time period; Indicates the minimum required duration; Indicates the unit exist Adjustable capacity for different time periods; This represents the minimum adjustable capacity proportional coefficient; Indicates the unit Maximum climbing rate; Indicates the unit exist The duration of the time slot can be flexibly adjusted. Indicates the minimum required flexibility duration; Indicates the maximum allowed price change; Indicates the maximum permissible slope variation; This indicates net cost.

[0030] Furthermore, the construction of a VRR curve-related settlement model for multidimensional evaluation is considered, including: (12) in: Indicates the unit exist Market settlement amount for the specified time period; Indicates the unit exist The assessment amount for frequency modulation response during specific time periods; Indicates the unit exist The amount allocated to assess system flexibility during specific time periods; Indicates the unit exist The amount assessed based on price fluctuations over a given period.

[0031] The capacity market VRR curve adaptive optimization method of this invention starts from the design of the capacity market mechanism, aims at optimizing system operating efficiency, considers the multi-dimensional performance characteristics of the generating units, and establishes a comprehensive evaluation system that includes system flexibility, frequency regulation capability and capacity reliability. Through the adaptive optimization design of the VRR curve, it effectively copes with the uncertainty brought about by the large-scale access of new energy sources, improves the efficiency of system resource allocation, and realizes the accurate transmission of capacity market price signals.

[0032] In summary, the beneficial effects of the present invention are as follows: This invention aims to optimize system operating efficiency by establishing a comprehensive evaluation system for frequency regulation response capability and system flexibility, thereby achieving dynamic optimization of the VRR curve. This method considers the performance characteristics of different types of generating units, including indicators such as response time, regulation accuracy, and sustainability, and establishes a differentiated evaluation mechanism. This ensures that the VRR curve accurately reflects the frequency regulation value and flexibility contribution of various resources. Through adaptive optimization design, the capacity market can effectively cope with the uncertainties brought about by the integration of new energy sources, improving the efficiency of system resource allocation. This is of great significance for enhancing the frequency regulation capability of the power system, improving system flexibility, and promoting the consumption of new energy.

[0033] To implement the methods of the above embodiments, the present invention also provides a capacity market VRR curve adaptive optimization system 10, such as... Figure 2 As shown, it includes: The parameter acquisition module 100 is used to acquire system operating parameters, including unit frequency regulation characteristics, system flexibility indicators and capacity reliability indicators. The constraint construction module 200 is used to construct, respectively, the objective function for adaptive optimization of the VRR curve, the system's normal operation constraints, and the VRR curve-related constraints for multidimensional evaluation based on the system's operating parameters. The model solving module 300 is used to construct a dynamically adjusted model based on the objective function, system operating constraints, and VRR curve related constraints, and solve for the optimized VRR curve parameters.

[0034] The capacity market VRR curve adaptive optimization system of this invention starts from the design of the capacity market mechanism, aims at optimizing system operating efficiency, considers the multi-dimensional performance characteristics of the generating units, and establishes a comprehensive evaluation system that includes system flexibility, frequency regulation capability and capacity reliability. Through the adaptive optimization design of the VRR curve, it effectively copes with the uncertainty brought about by the large-scale access of new energy sources, improves the efficiency of system resource allocation, and realizes the accurate transmission of capacity market price signals.

[0035] To implement the methods of the above embodiments, the present invention also provides a computer device, such as... Figure 3 As shown, the computer device 600 includes a memory 601 and a processor 602; wherein, the processor 602 reads executable program code stored in the memory 601 to run a program corresponding to the executable program code, so as to implement the various steps of the method described above.

[0036] To implement the above embodiments, this application also proposes a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the method described in the foregoing embodiments.

[0037] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

[0038] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0039] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. An adaptive optimization method for capacity market VRR curves, characterized in that, Includes the following steps: Obtain system operating parameters, including unit frequency regulation characteristics, system flexibility indicators, and capacity reliability indicators; Based on the system operating parameters, construct the objective function for adaptive optimization of the VRR curve, the system's normal operating constraints, and the VRR curve-related constraints for multidimensional evaluation. A dynamic adjustment model is constructed based on the objective function, system operating constraints, and VRR curve-related constraints, and the optimized VRR curve parameters are obtained by solving the model.

2. The method according to claim 1, characterized in that, Construct the objective function for adaptive optimization of the VRR curve, including: (1) in, , They represent the generating units. exist The price and quantity of capacity for each time period; Indicates the unit exist Frequency modulation response capability indicators for different time periods; Indicates the unit exist System flexibility indicators for different time periods; , , These are the penalty coefficients.

3. The method according to claim 1, characterized in that, Establish system routine operational constraints, including: Capacity adequacy constraints: (2) Unit capacity upper and lower limit constraints: (3) Unit availability constraints: (4) VRR curve slope constraint: (5) Price upper and lower limits constraints: (6) Frequency modulation response capability constraints: (7) System flexibility constraints: (8) in, Indicates the unit exist Capacity during a given time period; express System capacity requirements for a given time period; , They represent the generating units. Minimum and maximum capacity limits; Indicates the unit The installed capacity; Indicates the unit The equivalent forced shutdown rate; Indicates the unit exist Capacity pricing for specific time periods; , These represent the minimum and maximum allowable slopes of the VRR curve, respectively; , These represent the lower and upper limits of the capacity price, respectively. Indicates the unit exist Frequency modulation response capability indicators for different time periods; This indicates the minimum frequency regulation response capability required by unit i; Indicates the unit exist System flexibility indicators for different time periods; Indicates the unit The minimum required level of system flexibility.

4. The method according to claim 1, characterized in that, Constructing constraints related to the VRR curve for multidimensional evaluation, including: Frequency modulation response capability evaluation constraints: (9) System flexibility assessment constraints: (10) VRR curve characteristic constraints: (11) in: Indicates the unit exist Frequency modulation response time for a given period; Indicates the maximum allowed response time; Indicates the unit exist Frequency adjustment deviation during the time period; Indicates the maximum permissible frequency deviation; Indicates the unit exist The duration of frequency modulation during the time period; Indicates the minimum required duration; Indicates the unit exist Adjustable capacity for different time periods; This represents the minimum adjustable capacity proportional coefficient. Indicates the unit Maximum climbing rate; Indicates the unit exist The duration of the time slot can be flexibly adjusted. Indicates the minimum required flexibility duration; Indicates the maximum allowed price change; Indicates the maximum permissible slope variation; This indicates net cost.

5. The method according to claim 1, characterized in that, Constructing a dynamic adjustment model for the VRR curve includes: (12) in: Indicates the unit exist Market settlement amount for the specified time period; Indicates the unit exist The assessment amount for frequency modulation response during specific time periods; Indicates the unit exist The amount allocated to assess system flexibility during specific time periods; Indicates the unit exist The amount assessed based on price fluctuations over a given period.

6. A capacity market VRR curve adaptive optimization system, characterized in that, Includes the following steps: The parameter acquisition module is used to acquire system operating parameters, including unit frequency regulation characteristics, system flexibility indicators, and capacity reliability indicators. The constraint construction module is used to construct, based on the system operating parameters, the objective function for adaptive optimization of the VRR curve, the system's normal operating constraints, and the VRR curve-related constraints for multidimensional evaluation. The model solving module is used to construct a dynamically adjusted model based on the objective function, system operating constraints, and VRR curve-related constraints, and solve for the optimized VRR curve parameters.

7. A computer device, characterized in that, Including processor and memory; The processor reads executable program code stored in the memory to run a program corresponding to the executable program code, in order to implement the capacity market VRR curve adaptive optimization method considering multidimensional evaluation as described in any one of claims 1-5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the capacity market VRR curve adaptive optimization method considering multidimensional evaluation as described in any one of claims 1-5.

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