Method and system for evaluating medium-and-long-term regulation capability of power system

Abstract

The invention discloses a method and a system for evaluating medium-and-long-term regulation capability of a power system. The method comprises the following steps: acquiring current annual adjustable resource parameters and source-load annual prediction data; according to the adjustable resource parameters of the current year, establishing a multi-dimensional index for quantifying the medium-and-long-term adjustment capability of the system and a system adjustment capability quantification model; constructing a system medium-and-long-term regulation capability evaluation model embedded with a system frequency modulation capability quantification model, so as to meet the requirement that the quantification values of a conventional regulation capability index RCsys and a post-disturbance frequency modulation capability index FRCsys are minimum as an evaluation target; based on a frequency modulation parameter in the adjustable resource parameters of the current year, reconstructing a nonlinear frequency modulation capability index so as to convert the medium-and-long-term regulation capability evaluation model of the system into a linear form, and evaluating after solving to obtain medium-and-long-term regulation capability demand distribution of the power system considering the frequency modulation capability; the system comprises a data processing module, an index and quantitative model building module, an evaluation model building module and an evaluation model solving module.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a method and system for evaluating the long-term regulation capability of a power system. Background Art

[0002] The continuous increase in the proportion of volatile and intermittent renewable power generation resources has brought significant challenges to the power system in maintaining the balance between supply and demand, and the risk of load loss and power abandonment has increased. At the same time, the reduction in the system frequency regulation capability caused by the replacement of traditional units by a large number of renewable generators with converter interfaces has also affected the frequency stability of the system under disturbances. In recent years, there have been several incidents of large frequency drops in high-proportion renewable energy power grids around the world, resulting in a large number of load cuts. It can be seen that the regulation capacity of the system's existing flexible adjustable resources is insufficient to cope with a high-proportion renewable energy power system, and the system's regulation capacity will continue to decline as the proportion of renewable energy increases. Therefore, a reasonable quantitative assessment of the system's regulation capacity requirements is the basis for enhancing the flexibility and regulation capacity of the future power system and ensuring the stable operation of the system.

[0003] At present, the research on the quantitative evaluation of the system's flexibility regulation capability mainly focuses on the load and new energy level that the existing resources can cope with within a specific operating section or day-ahead scale of the system, that is, quantifying the available regulation boundary of the existing adjustable resources, but ignoring the quantification of the system regulation capability demand during the period when the net load exceeds the regulation boundary of the existing adjustable units through load shedding and power abandonment. In the medium- and long-term scenarios of unit expansion planning, there are usually multiple operating scenarios in which the system demand exceeds the boundary of the existing units. Therefore, how to effectively evaluate the system regulation capability demand that exceeds the regulation boundary of the existing adjustable units in the medium- and long-term scenarios still lacks a clear quantitative method. In addition, another major drawback of the existing work is that it ignores the quantification of the system's frequency regulation capability. Only a few studies use the traditional rotating reserve coefficient model to determine the total reserve of the system, but ignore the frequency dynamic response process of the system when it is disturbed. This method may overestimate the system's frequency regulation capability, which will lead to poor frequency interference immunity of the system. Summary of the invention

[0004] In view of this, the present invention provides a method and system for evaluating the medium- and long-term regulation capability of an electric power system, which is used to at least solve the problem that the electric power system in the prior art still lacks a method for evaluating the medium- and long-term regulation capability demand and cannot quantify the precise frequency regulation demand to ensure the frequency stability of the system under certain disturbances; the present invention provides a method for evaluating the medium- and long-term regulation capability of an electric power system for medium- and long-term scenarios such as future electric power system planning scenarios, comprehensively considers the limitations of the spatiotemporal coupling operation characteristics of the electric power system, constructs multi-dimensional quantitative indicators of medium- and long-term system regulation demand and corresponding quantitative evaluation methods, and the system frequency regulation capability demand under the expected disturbance can be intuitively quantified through the power form indicators of the present invention. The present invention can help power grid operators identify the detailed distribution of the system's multi-dimensional regulation capability shortage and the corresponding frequency regulation effect under various future operation scenarios, and then assist in guiding the power grid to determine a reasonable frequency resource planning layout and operation plan, and provide support for the stable operation of the power system.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: A method for evaluating the long-term regulation capability of a power system comprises the following steps: S1. Obtain the current annual adjustable resource parameters and source-load annual forecast data. The source-load annual forecast data is used to generate medium- and long-term typical scenarios as input data for the system's medium- and long-term regulation capacity evaluation model through the k-means clustering algorithm; S2. Based on the current annual adjustable resource parameters, establish a multi-dimensional indicator model to quantify the long-term and long-term adjustment capacity of the system; the multi-dimensional indicators include conventional adjustment capacity indicators to ensure supply and demand balance and the post-disturbance frequency regulation capability index considering the system frequency dynamics ; S3. Construct a system medium- and long-term regulation capability assessment model with a built-in system frequency modulation amplitude index model to fully meet the conventional regulation capability indicators required by the medium- and long-term system. and post-disturbance frequency modulation capability index The minimum quantitative value of is the evaluation target; S4. Based on the frequency regulation parameters in the current annual adjustable resource parameters, the nonlinear frequency regulation capability index is reconstructed in combination with the data-driven method to transform the system's long-term regulation capability evaluation model into a linear form. After solving the problem, the medium- and long-term regulation capability demand distribution of the power system considering the frequency regulation capability is obtained.

[0006] Preferably, the adjustable resource parameters in S1 are the unit technical parameters of the adjustable resources under the conventional grid-connected strategy, wherein the unit technical parameters of the adjustable resources under the conventional grid-connected strategy include but are not limited to: installed capacity, ramping, minimum technical output and frequency regulation parameters of various types of adjustable resources; the source-load annual forecast data includes the annual forecast data of the estimated installed capacity, output and load demand of new energy units in any planned year in the future.

[0007] Preferably, in S2, the system-level conventional regulation capability indicator Specifically includes upward and downward adjustment range and and system-level upward and downward adjustment speed and , the system-level post-disturbance frequency regulation capability indicator Includes system-level FM amplitude metrics : ; ; ; In the formula, , , and These are all general regulation capability indicators at the node level. and Respectively Time Node Adjustable resources The upward and downward adjustment range, and Respectively Time Node Adjustable resources Up and down adjustment speed; is the frequency modulation index at the node level, i.e. Adjustable resources exist The frequency modulation amplitude index at a certain moment is used to quantify the additional maximum frequency modulation power required to support the system to achieve the frequency modulation effect under the expected disturbance.

[0008] Preferably, the system medium- and long-term regulation capability evaluation model of S3 is specifically: ; The constraints include: constraints in normal operation state and post-fault state; The constraints of the normal operation state include a quantitative model of the normal regulation capability indicators at the node level; The constraints in the post-fault state include: the frequency modulation amplitude index at the node level after considering the frequency dynamics Quantitative model, including The system equivalent frequency modulation parameter model and the inclusion of The frequency modulation effect index includes the frequency change rate at the initial moment. , frequency deviation extreme value and quasi-steady-state frequency .

[0009] Preferably, the quantitative model of the conventional regulation capability index at the node level is: ; in, for The system demand level and existing resources work together at all times. for Line transmission power at all times; represents the system operation limit equation, represents the operating characteristic model of the adjustable unit incorporating the conventional regulation capability index, Indicates other operational characteristics of adjustable resources. The system operation restriction model is composed of system network security, load demand and restrictions on non-adjustable new energy units.

[0010] Preferably, based on the thermal power unit, the frequency modulation amplitude index at the node level The quantitative model is: ; in, is the unit response power during the system frequency response transient process The maximum value of For Node Thermal power unit exist Start-stop status; For Node Thermal power unit Maximum output limit; Inclusion The system equivalent frequency modulation parameter model is: ; ; In the formula, and All included The system equivalent frequency modulation parameters represent the medium and long term scenarios. The system equivalent inertia and speed regulator adjustment coefficient at the moment, is the reference power, It is a collection of thermal power units in the system. For Node Thermal power unit The inertia time constant, For Node Thermal power unit The speed regulator's adjustment factor; Frequency change rate at initial moment , frequency deviation extreme value and quasi-steady-state frequency The constraints are: ; ; ; in, For the system The active disturbance at the moment, for The maximum safety limit, is the system load damping coefficient, , , are the intermediate parameters that couple the nonlinear relationship of the system equivalent frequency modulation parameters, is the time when the system frequency deviation reaches the lowest point, is the safety limit of the lowest frequency point indicator, It is the safety limit of the quasi-steady-state frequency indicator.

[0011] Preferably, obtain the frequency deviation extreme value The specific contents include: The system frequency dynamics is described by the swing equation when the system is disturbed. When the thermal power unit is the main adjustable unit, the system frequency dynamic response model is: ; in, for When the system frequency deviation, is the load damping coefficient, for Time Node Thermal power unit Primary frequency modulation active power adjustment amount; Considering the prime mover-speed governor and virtual synchronous machine droop control link of synchronous unit, the power is adjusted by primary frequency regulation. The Laplace domain expression of is: ; in, For Node The turbine coefficient of the synchronous unit turbine is For Node Time constant of reheater of synchronous unit; Then the equivalent frequency dynamic analytical model of the system frequency dynamics is obtained as follows: ; ; in, is the system frequency deviation, is the system load damping coefficient; Obtained from the equivalent frequency dynamic analytical model of system frequency dynamics The frequency deviation extreme value corresponding to the moment : .

[0012] Preferably, the specific content of S4 includes: (1) Combine the frequency modulation parameters in the current annual adjustable resource parameters and the quantifiable upper limit of the frequency modulation amplitude index to obtain the original range of the system equivalent frequency modulation parameters: and ,in, and They are the system equivalent frequency modulation parameters and The upper and lower limits of the sampling are generated within the original range. and , and is calculated based on the equivalent frequency dynamic analytical model , generating a sample point surface; wherein the frequency modulation parameters in the current annual adjustable resource parameters include but are not limited to: installed capacity and inertia time constant, speed regulator adjustment coefficient and response time constant; (2) Obtaining satisfaction based on data-driven classification methods The sample point range within the range is then tightened to the original range, and then the original range is converted to the tightened range and Replace, where and They are and The value after compression; (3) Reconstruction using linear approximation linearization method ; Will The quantitative model is finally transformed into an equivalent frequency modulation parameter that can be easily incorporated into the system and The linear combination form is: ; in, For the The approximation error corresponding to the sample points is For the range after tightening; Ensure that the approximate linear result is above the constructed observation surface, and are the linear fitting coefficients respectively; Then the nonlinear frequency deviation extreme value Finally, it is reconstructed into a linear model that characterizes the boundary range of the frequency modulation parameters: ; The reconstructed linear form model replaces the original nonlinear frequency deviation extreme value The final system medium- and long-term regulation capability assessment model is obtained, and the solution to the final system medium- and long-term regulation capability assessment model is completed.

[0013] A system for evaluating the medium and long-term regulation capability of a power system, comprising: The data processing module is used to obtain the current annual adjustable resource parameters and the source-load annual forecast data. The source-load annual forecast data generates medium- and long-term typical scenarios through the k-means clustering algorithm as the input data of the system's medium- and long-term regulation capacity evaluation model; The indicator and quantitative model building module is used to establish a multi-dimensional indicator model for the long-term adjustment capacity of the quantitative system based on the current annual adjustable resource parameters; the multi-dimensional indicators include conventional adjustment capacity indicators to ensure supply and demand balance and the post-disturbance frequency regulation capability index considering the system frequency dynamics ; Evaluation model building module, used to build a system medium- and long-term regulation capability evaluation model with a built-in system frequency modulation amplitude index model, so as to fully meet the conventional regulation capability indicators required by the medium- and long-term system. and post-disturbance frequency modulation capability index The minimum quantitative value of is the evaluation target; The evaluation model solving module is used to reconstruct the nonlinear frequency regulation capability index based on the frequency regulation parameters in the current annual adjustable resource parameters in combination with the data-driven method to transform the system's long-term regulation capability evaluation model into a linear form. After solving, the medium- and long-term regulation capability demand distribution of the power system considering the frequency regulation capability is evaluated.

[0014] It can be seen from the above technical solutions that, compared with the prior art, the present invention discloses a method and system for evaluating the medium and long-term regulation capability of a power system, which has the following beneficial effects: The present invention comprehensively considers the limitations of the spatiotemporal coupling operation characteristics of the power system, constructs a multi-dimensional index and a corresponding quantitative evaluation method that can clearly quantify the medium- and long-term system regulation requirements, and can accurately quantify the frequency regulation capacity requirements in the dynamic process of the system frequency response. The system frequency regulation capacity requirements under the expected disturbance can be intuitively quantified and presented through the power form of the index of the present invention, which solves the problem that the current power system cannot accurately quantify the regulation capacity requirements of the system during the period of insufficient regulation capacity in the future medium- and long-term scenarios. The present invention can identify the detailed distribution of the system's multi-dimensional regulation capacity shortage and the corresponding frequency regulation effect under various medium- and long-term operation scenarios, and then assist in guiding the power grid to determine a reasonable multi-type regulation resource planning layout and operation plan, which can effectively avoid the orderly load shedding caused by the imbalance of supply and demand after the large-scale new energy grid connection in the future, the abandonment of new energy, and the low-frequency load reduction that may be caused by insufficient system frequency regulation capacity under short-term high-power shocks. Provide strong support for the stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A schematic flow chart of a method for evaluating the medium- and long-term regulation capability of a power system provided by the present invention; Figure 2 A topological diagram of a test system provided by an embodiment of the present invention; Figure 3 A time distribution diagram of the quantified results of the multi-dimensional indicators of the long-term regulation capability of the system provided in the embodiment of the present invention; Figure 3 (a) , and Time distribution diagram of Figure 3 (b) and Time distribution diagram of Figure 3 (c) , , , , and Time distribution diagram of Figure 4 A comparison chart of the quantized results of the system frequency modulation amplitude index provided by an embodiment of the present invention; Figure 5 A comparison chart of frequency simulation results provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions 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.

[0018] The present invention provides a method for evaluating the long-term regulation capability of a power system. Figure 1 As shown, the following steps are included: S1. Obtain the current annual adjustable resource parameters and source-load annual forecast data. The source-load annual forecast data is used to generate medium- and long-term typical scenarios as input data for the system's medium- and long-term regulation capacity evaluation model through the k-means clustering algorithm; S2. Based on the current annual adjustable resource parameters, establish a multi-dimensional indicator model to quantify the long-term and long-term adjustment capacity of the system; the multi-dimensional indicators include conventional adjustment capacity indicators to ensure supply and demand balance and the post-disturbance frequency regulation capability index considering the system frequency dynamics ; S3. Construct a system medium- and long-term regulation capability assessment model with a built-in system frequency modulation amplitude index model to fully meet the conventional regulation capability indicators required by the medium- and long-term system. and post-disturbance frequency modulation capability index The minimum quantitative value of is the evaluation target; S4. Based on the frequency regulation parameters in the current annual adjustable resource parameters, the nonlinear frequency regulation capability index is reconstructed in combination with the data-driven method to transform the system's long-term regulation capability evaluation model into a linear form. After solving the problem, the medium- and long-term regulation capability demand distribution of the power system considering the frequency regulation capability is obtained.

[0019] In order to further implement the above technical scheme, the adjustable resource parameters in S1 are the unit technical parameters of the adjustable resources under the conventional grid-connected strategy, wherein the unit technical parameters of the adjustable resources under the conventional grid-connected strategy include but are not limited to: installed capacity, ramping, minimum technical output and frequency regulation parameters of various adjustable resources; the source-load annual forecast data includes the annual forecast data of the estimated installed capacity, output and load demand of new energy units in any planned year in the future.

[0020] It should be noted that: The current annual adjustable resource parameters are the installed capacity, ramp rate, minimum technical output, frequency regulation parameters and other unit technical parameters of various adjustable resources such as thermal power, hydropower, energy storage, etc., except for wind and solar units that cannot be adjusted under conventional grid-connected strategies.

[0021] In order to further implement the above technical solution, in S2, the conventional regulation capability index of the system level Specifically includes upward and downward adjustment range and and system-level upward and downward adjustment speed and , the system-level post-disturbance frequency regulation capability indicator Includes system-level FM amplitude metrics : ; ; ; In the formula, , , and These are all general regulation capability indicators at the node level. and Respectively Time Node Adjustable resources The upward and downward adjustment range, and Respectively Time Node Adjustable resources Up and down adjustment speed; is the frequency modulation index at the node level, i.e. Adjustable resources exist The frequency modulation amplitude index at a certain moment is used to quantify the additional maximum frequency modulation power required to support the system to achieve the frequency modulation effect under the expected disturbance.

[0022] It should be noted that: In order to fully consider the spatiotemporal coupling characteristics of the system regulation capability to ensure the feasibility of the quantified system regulation capability, the total power that needs to be additionally injected or outflowed to meet the regulation needs of the system during the period of insufficient regulation capability is decomposed from the two aspects of amplitude and speed. Then the conventional regulation capability indicators include Time Node Adjustable resources Up and down adjustment range and up, down to adjust the speed .

[0023] In order to further implement the above technical solutions, the system long-term regulation capability evaluation model of S3 is as follows: ; The constraints include: constraints in normal operation state and post-fault state; The constraints of the normal operation state include a quantitative model of the normal regulation capability indicators at the node level; The constraints in the post-fault state include: the frequency modulation amplitude index at the node level after considering the frequency dynamics Quantitative model, including The system equivalent frequency modulation parameter model and the inclusion of The frequency modulation effect index includes the frequency change rate at the initial moment. , frequency deviation extreme value and quasi-steady-state frequency .

[0024] In order to further implement the above technical solution, the quantitative model of the conventional regulation capability index at the node level is: ; in, for The system demand level and existing resources work together at all times. for Line transmission power at all times; represents the system operation limit equation, represents the operating characteristic model of the adjustable unit incorporating the conventional regulation capability index, Indicates other operational characteristics of adjustable resources. The system operation restriction model is composed of system network security, load demand and restrictions on non-adjustable new energy units.

[0025] It should be noted that: The operating characteristics of the adjustable units and the system operating limit models are all modeled using a general model. Among them, the system operating limits include system network trends and non-adjustable new energy units and load demand limits. The adjustable units include but are not limited to thermal power units, hydropower units and other adjustable units such as energy storage. The specific adjustable unit type depends on the combination of adjustable unit types in the current year of the system to be evaluated. This embodiment takes a thermal power unit as an example, and its model after incorporating the adjustment capacity index is: ; ; ; ; in, For Node Thermal power unit exist The output of time, For thermal power units Output limitation and up and down climbing, For Node Thermal power unit exist Start-stop status.

[0026] Other operating characteristic models of thermal power units It includes the start-stop characteristic model that characterizes its unit combination, specifically: ; ; ; ; ; in, For Node Thermal power unit The start-stop action, It is the minimum start and stop time of thermal power units.

[0027] In order to further implement the above technical solution, the frequency modulation amplitude index quantifies the additional maximum frequency modulation power required to support the system to achieve the frequency modulation effect under the expected disturbance. In order to accurately quantify the frequency modulation capability of the system after the fault, the frequency modulation amplitude index The quantification process of the system frequency dynamic process must be considered, and the node frequency modulation amplitude parameter is included based on the node where the thermal power unit is located. , which quantifies the power deficit required for the units participating in frequency regulation to have sufficient frequency regulation response power, that is, The remaining power processing after the power generation can just meet the maximum frequency modulation response power. Taking the thermal power unit as the benchmark, the frequency modulation amplitude index at the node level is The quantitative model is: ; in, is the unit response power during the system frequency response transient process The maximum value of the system quasi-steady-state frequency and node Thermal power unit The speed regulator adjustment coefficient Make estimates; For Node Thermal power unit exist Start-stop status; For Node Thermal power unit Maximum output limit; Inclusion The system equivalent frequency modulation parameter model is: ; ; In the formula, and All included The system equivalent frequency modulation parameters represent the medium and long term scenarios. The system equivalent inertia and speed regulator adjustment coefficient at the moment, is the reference power, It is a collection of thermal power units in the system. For Node Thermal power unit The inertia time constant, For Node Thermal power unit The speed regulator's adjustment factor; Frequency change rate at initial moment , frequency deviation extreme value and quasi-steady-state frequency The constraints are: ; ; , in, For the system The active disturbance at the moment, for The maximum safety limit, is the system load damping coefficient, are the intermediate parameters that couple the nonlinear relationship of the system equivalent frequency modulation parameters, is the time when the system frequency deviation reaches the lowest point, is the safety limit of the lowest frequency point indicator, It is the safety limit of the quasi-steady-state frequency indicator.

[0028] In order to further implement the above technical solution, obtain the frequency deviation extreme value The specific contents include: The system frequency dynamics is described by the swing equation when the system is disturbed. When the thermal power unit is the main adjustable unit, the system frequency dynamic response model is: ; in, for When the system frequency deviation, is the load damping coefficient, for Time Node Thermal power unit Primary frequency modulation active power adjustment amount; Considering the prime mover-speed governor and virtual synchronous machine droop control link of synchronous unit, the power is adjusted by primary frequency regulation. The Laplace domain expression of is: ; in, For Node The turbine coefficient of the synchronous unit turbine is For Node Time constant of reheater of synchronous unit; Then the equivalent frequency dynamic analytical model of the system frequency dynamics is obtained as follows: ; ; in, is the system frequency deviation, is the system load damping coefficient; Obtained from the equivalent frequency dynamic analytical model of system frequency dynamics The frequency deviation extreme value corresponding to the moment : .

[0029] In order to further implement the above technical solution, the specific contents of S4 include: (1) Combine the frequency modulation parameters in the current annual adjustable resource parameters and the quantifiable upper limit of the frequency modulation amplitude index to obtain the original range of the system equivalent frequency modulation parameters: and ,in, and They are the system equivalent frequency modulation parameters and The upper and lower limits of the sampling are generated within the original range. and , and is calculated based on the equivalent frequency dynamic analytical model , generating a sample point surface; wherein the frequency modulation parameters in the current annual adjustable resource parameters include but are not limited to: installed capacity and inertia time constant, speed regulator adjustment coefficient and response time constant; (2) Obtaining satisfaction based on data-driven classification methods The sample point range within the range is then tightened to the original range, and then the original range is converted to the tightened range and Replace, where and They are and The value after compression; (3) Reconstruction using linear approximation linearization method ; Will The quantitative model is finally transformed into an equivalent frequency modulation parameter that can be easily incorporated into the system and The linear combination form is: ; in, For the The approximation error corresponding to the sample points is For the range after tightening; Ensure that the approximate linear result is above the constructed observation surface, and are the linear fitting coefficients respectively; Then the nonlinear frequency deviation extreme value Finally, it is reconstructed into a linear model that characterizes the boundary range of the frequency modulation parameters: ; The reconstructed linear form model replaces the original nonlinear frequency deviation extreme value The final system medium- and long-term regulation capability assessment model is obtained, and the solution to the final system medium- and long-term regulation capability assessment model is completed.

[0030] It should be noted that: Since the frequency deviation extreme value indicator The model is a highly nonlinear coupling relationship about the system equivalent frequency modulation parameters, which makes the evaluation model unsolvable. Therefore, the above method is proposed to reconstruct the nonlinear part and then solve the evaluation model.

[0031] A system for evaluating the medium and long-term regulation capability of a power system, comprising: The data processing module is used to obtain the current annual adjustable resource parameters and the source-load annual forecast data. The source-load annual forecast data generates medium- and long-term typical scenarios through the k-means clustering algorithm as the input data of the system's medium- and long-term regulation capacity evaluation model; The indicator and quantitative model building module is used to establish a multi-dimensional indicator model for the long-term adjustment capacity of the quantitative system based on the current annual adjustable resource parameters; the multi-dimensional indicators include conventional adjustment capacity indicators to ensure supply and demand balance and the post-disturbance frequency regulation capability index considering the system frequency dynamics ; Evaluation model building module, used to build a system medium- and long-term regulation capability evaluation model with a built-in system frequency modulation amplitude index model, so as to fully meet the conventional regulation capability indicators required by the medium- and long-term system. and post-disturbance frequency modulation capability index The minimum quantitative value of is the evaluation target; The evaluation model solving module is used to reconstruct the nonlinear frequency regulation capability index based on the frequency regulation parameters in the current annual adjustable resource parameters in combination with the data-driven method to transform the system's long-term regulation capability evaluation model into a linear form. After solving, the medium- and long-term regulation capability demand distribution of the power system considering the frequency regulation capability is evaluated.

[0032] The present invention will be further described below through experiments: The annual frequency modulation amplitude indicator evaluation of the improved IEEE HRP-38 system stock resources in future planning scenarios is taken as an example to illustrate.

[0033] Figure 2 To test the system topology, the total capacity of the system's synchronous units, WT and PV was modified to 140.14GW, 60.72GW and 101.14GW, with renewable energy accounting for more than 50%. The annual source-load forecast data was generated for 8 typical days using the K-means clustering method. The boundary of the frequency modulation amplitude indicator and 5500MW and 3000MW respectively, the boundary of frequency modulation effect , and Settings are 0.5Hz / s, 0.5Hz and 0.3Hz.

[0034] 1. Results of the mid- to long-term regulatory capacity assessment Based on the regulatory capacity evaluation method of the present invention, the mid- and long-term regulatory capacity evaluation results of the HRP-38 system are obtained. The time distribution of the multi-dimensional indicators of the system is as follows: Figure 3 As shown in the figure, the system increase rate in most of the time periods on typical days 1, 6 and 8, which represent high system demand Insufficient, indicating that the existing resources cannot meet the load demand. Most of the time periods in other scenarios with relatively small load levels do not exist. The shortage is only a small amount of 19-23h and 21-24h on the high-load demand of typical days 4 and 5, which represent the medium load level throughout the year. Quantization value. Frequency modulation amplitude There are deficiencies during high load periods in all scenarios due to The system frequency modulation capability during the period is completely determined by undertake, therefore The quantization value is larger. The stock resource regulation capacity during the period (e.g. 20-22h on a typical day 2) can meet the system demand and has some available frequency modulation range, but it is not enough to resist the expected disturbance. The system frequency regulation capacity requirements in addition to the available frequency regulation capacity of existing resources are quantified. At 15h and 16h on the typical day 7, which represents the lowest system demand throughout the year, the system's downward regulation range is There are shortcomings.

[0035] As for the system adjustment speed index, in some typical scenarios with the fastest load reduction (increase) speed, there are The demand quantification value indicates that the rapid changes in system demand during these periods exceed the adjustment speed of existing resources. Figure 3 (c) is the system frequency regulation effect indicator for a specific typical scenario. It can be seen that after incorporating the quantitative value of the system frequency regulation demand, the frequency regulation effect of the system in each period is guaranteed to be within the limit.

[0036] 2. Verification of the effectiveness of the system frequency modulation capability quantification method In order to further verify the effectiveness of the frequency modulation capability quantification method in the system regulation capability evaluation method proposed in the present invention and the necessity of considering frequency dynamics and frequency modulation capability quantification, the following three methods are set up for comparison: Proposed method: The frequency modulation capability quantification method proposed in the present invention; Traditional method 1: Without considering the system frequency dynamic model, the traditional spinning reserve model is used to quantify the system frequency modulation amplitude , the reserve factor is set to 5% load; Traditional method 2: System regulation capability evaluation without considering the system frequency regulation capability, the system frequency dynamics and the quantitative model of frequency regulation capability are ignored.

[0037] The quantitative results of the system adjustment amplitude are as follows: Figure 4 As shown in Figure 2, the traditional method 2 that does not consider the system frequency regulation capability has no frequency regulation capability quantification. The traditional method 1 does not consider the frequency dynamics, and only the Case 2 with a 5% load reserve factor is obtained. The evaluation results at a certain moment were selected to conduct a system frequency dynamic simulation to further verify the frequency modulation effect of the proposed frequency modulation capability quantification method and the necessity of considering frequency dynamics. The simulation results are shown in Figure 5 The proposed method considering frequency dynamics can quantify the frequency regulation demand of the units. After the adjustable units are included in the quantified results, they all have sufficient frequency regulation capabilities. Therefore, the adjustable units all participate in the system frequency response, and their response power is quantified. within the limit. The simulation also shows that the proposed quantification method effectively guarantees the frequency safety of the system. However, the adjustable units of the traditional method 2 that does not consider the quantification of the system frequency regulation capacity have no frequency regulation amplitude space. Although the B2 and B4 nodes in the traditional method quantify part of the frequency regulation capacity requirements, the spatial distribution of the frequency regulation amplitude index is unreasonable due to the failure to consider the frequency dynamics of the units, and the units at B2 and B4 have no available adjustment speed. Therefore, the amplitude limits of the adjustable units of the two traditional methods are both 0 and cannot participate in the primary frequency regulation, and only provide inertial response. Both cannot guarantee the frequency stability of the system under the same disturbance. Therefore, it can be seen that the adjustment capacity quantification method proposed in the present invention can effectively quantify the shortage of the system frequency regulation capacity, effectively guarantee the system frequency safety after incorporating the quantified value of the frequency regulation capacity, and provide support for the stable operation of the system.

[0038] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for evaluating the medium- and long-term regulation capability of a power system, characterized in that: The following steps are involved: S1. Obtain the current annual adjustable resource parameters and source-load annual forecast data. The source-load annual forecast data is used to generate medium- and long-term typical scenarios as input data for the system's medium- and long-term regulation capacity evaluation model through the k-means clustering algorithm; S2. Based on the current annual adjustable resource parameters, establish a multi-dimensional indicator model to quantify the long-term and long-term adjustment capacity of the system; the multi-dimensional indicators include conventional adjustment capacity indicators to ensure supply and demand balance and the post-disturbance frequency regulation capability index considering the system frequency dynamics ; S3. Construct a system medium- and long-term regulation capability assessment model with a built-in system frequency modulation amplitude index model to fully meet the conventional regulation capability indicators required by the medium- and long-term system. and post-disturbance frequency modulation capability index The minimum quantitative value of is the evaluation target; S4. Based on the frequency regulation parameters in the current annual adjustable resource parameters, the nonlinear frequency regulation capability index is reconstructed in combination with the data-driven method to transform the system's long-term regulation capability evaluation model into a linear form. After solving the problem, the medium- and long-term regulation capability demand distribution of the power system considering the frequency regulation capability is obtained.

2. A method for evaluating the medium- and long-term regulation capability of a power system according to claim 1, characterized in that: The adjustable resource parameters in S1 are the unit technical parameters of the adjustable resources under the conventional grid-connected strategy, where the unit technical parameters of the adjustable resources under the conventional grid-connected strategy include but are not limited to: installed capacity, ramping, minimum technical output and frequency regulation parameters of various adjustable resources; the source-load annual forecast data includes the annual forecast data of the estimated installed capacity of new energy units, the output of new energy units and load demand in any planned year in the future.

3. A method for evaluating the medium- and long-term regulation capability of a power system according to claim 1, characterized in that: In S2, the general regulation capability indicators at the system level The specific adjustment range includes upward and downward adjustment and and system-level upward and downward adjustment speed and , the system-level post-disturbance frequency regulation capability indicator Includes system-level FM amplitude metrics : ； ； ； In the formula, , , and These are all general regulation capability indicators at the node level. and Respectively Time Node Adjustable resources The upward and downward adjustment range, and Respectively Time Node Adjustable resources Up and down adjustment speed; is the frequency modulation index at the node level, i.e. Adjustable resources exist The frequency modulation amplitude index at a certain moment is used to quantify the additional maximum frequency modulation power required to support the system to achieve the frequency modulation effect under the expected disturbance.

4. A method for evaluating the medium- and long-term regulation capability of a power system according to claim 3, characterized in that: The specific evaluation model of S3's system long-term regulation capability is as follows: ； The constraints include: constraints in normal operation state and post-fault state; The constraints of the normal operation state include a quantitative model of the normal regulation capability indicators at the node level; The constraints in the post-fault state include: the frequency modulation amplitude index at the node level after considering the frequency dynamics Quantitative model, including The system equivalent frequency modulation parameter model and the inclusion of The frequency modulation effect index includes the frequency change rate at the initial moment. , frequency deviation extreme value and quasi-steady-state frequency .

5. A method for evaluating the medium- and long-term regulation capability of a power system according to claim 4, characterized in that: The quantitative model of the conventional regulation capability index at the node level is: ； in, for The system demand level and existing resources work together at all times. for Line transmission power at all times; represents the system operation limit equation, represents the operating characteristic model of the adjustable unit incorporating the conventional regulation capability index, Indicates other operational characteristics of adjustable resources. The system operation restriction model is composed of system network security, load demand and restrictions on non-adjustable new energy units.

6. A method for evaluating the medium- and long-term regulation capability of a power system according to claim 4, characterized in that: Based on the thermal power unit, the frequency modulation amplitude index at the node level The quantitative model is: ； in, is the unit response power during the system frequency response transient process The maximum value of For Node Thermal power unit exist Start-stop status; For Node Thermal power unit Maximum output limit; Inclusion The system equivalent frequency modulation parameter model is: ； ； In the formula, and All included The system equivalent frequency modulation parameters represent the medium and long term scenarios. The system equivalent inertia and speed regulator adjustment coefficient at the moment, is the reference power, It is a collection of thermal power units in the system. For Node Thermal power unit The inertia time constant, For Node Thermal power unit The speed regulator's adjustment factor; Frequency change rate at initial moment , frequency deviation extreme value and quasi-steady-state frequency The constraints are: ； ； ； in, For the system The active disturbance at the moment, for The maximum safety limit, is the system load damping coefficient, , , are the intermediate parameters that couple the nonlinear relationship of the system equivalent frequency modulation parameters, is the time when the system frequency deviation reaches the lowest point, is the safety limit of the lowest frequency point indicator, It is the safety limit of the quasi-steady-state frequency indicator.

7. A method for evaluating the medium- and long-term regulation capability of a power system according to claim 6, characterized in that: Get the frequency deviation extreme value The specific contents include: The system frequency dynamics is described by the swing equation when the system is disturbed. When the thermal power unit is the main adjustable unit, the system frequency dynamic response model is: ； in, for When the system frequency deviation, is the load damping coefficient, for Time Node Thermal power unit Primary frequency modulation active power adjustment amount; Considering the prime mover-speed governor and virtual synchronous machine droop control link of synchronous unit, the power is adjusted by primary frequency regulation. The Laplace domain expression of is: ； in, For Node The turbine coefficient of the synchronous unit turbine is For Node Time constant of reheater of synchronous unit; Then the equivalent frequency dynamic analytical model of the system frequency dynamics is obtained as follows: ； ； in, is the system frequency deviation, is the system load damping coefficient; Obtained from the equivalent frequency dynamic analytical model of system frequency dynamics The frequency deviation extreme value corresponding to the moment : 。 8. A method for evaluating the medium- and long-term regulation capability of a power system according to claim 6, characterized in that: The specific contents of S4 include: (1) Combine the frequency modulation parameters in the current annual adjustable resource parameters and the quantifiable upper limit of the frequency modulation amplitude index to obtain the original range of the system equivalent frequency modulation parameters: and ,in, and They are the system equivalent frequency modulation parameters and The upper and lower limits of the sampling are generated within the original range. and , and is calculated based on the equivalent frequency dynamic analytical model , generating a sample point surface; wherein the frequency modulation parameters in the current annual adjustable resource parameters include but are not limited to: installed capacity and inertia time constant, speed regulator adjustment coefficient and response time constant; (2) Obtaining satisfaction based on data-driven classification methods The sample point range within the range is then tightened to the original range, and then the original range is converted to the tightened range and Replace, where and They are and The value after compression; (3) Reconstruction using linear approximation linearization method ; Will The quantitative model is finally transformed into an equivalent frequency modulation parameter that can be easily incorporated into the system and The linear combination form is: ； in, For the The approximation error corresponding to the sample points is For the range after tightening; Ensure that the approximate linear result is above the constructed observation surface, and are the linear fitting coefficients respectively; Then the nonlinear frequency deviation extreme value Finally, it is reconstructed into a linear model that characterizes the boundary range of the frequency modulation parameters: ； The reconstructed linear form model replaces the original nonlinear frequency deviation extreme value The final system medium- and long-term regulation capability assessment model is obtained, and the solution to the final system medium- and long-term regulation capability assessment model is completed.

9. A system for evaluating the medium- and long-term regulation capability of a power system, characterized in that: include: The data processing module is used to obtain the current annual adjustable resource parameters and the source-load annual forecast data. The source-load annual forecast data generates medium- and long-term typical scenarios through the k-means clustering algorithm as the input data of the system's medium- and long-term regulation capacity evaluation model; The indicator and quantitative model building module is used to establish a multi-dimensional indicator model for the long-term adjustment capacity of the quantitative system based on the current annual adjustable resource parameters; the multi-dimensional indicators include conventional adjustment capacity indicators to ensure supply and demand balance and the post-disturbance frequency regulation capability index considering the system frequency dynamics ; Evaluation model building module, used to build a system medium- and long-term regulation capability evaluation model with a built-in system frequency modulation amplitude index model, so as to fully meet the conventional regulation capability indicators required by the medium- and long-term system. and post-disturbance frequency modulation capability index The minimum quantitative value of is the evaluation target; The evaluation model solving module is used to reconstruct the nonlinear frequency regulation capability index based on the frequency regulation parameters in the current annual adjustable resource parameters in combination with the data-driven method to transform the system's long-term regulation capability evaluation model into a linear form. After solving, the medium- and long-term regulation capability demand distribution of the power system considering the frequency regulation capability is evaluated.

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