A Medium- and Long-Term Regulation Capacity Evaluation Method and System for a Power System

Abstract

The present invention discloses a method and system for evaluating the medium- and long-term regulation capacity of a power system. The method includes: obtaining the adjustable resource parameters for the current year and the source-load annual forecast data; establishing a multi-dimensional index for quantifying the medium- and long-term regulation capacity of the system and a system regulation capacity quantification model based on the adjustable resource parameters for the current year; constructing a medium- and long-term system regulation capacity evaluation model embedded with a system frequency regulation capacity quantification model to meet the quantification values of the conventional regulation capacity index RC sys and the post-disturbance frequency regulation capacity index FRC sys with the minimum value as the evaluation target; reconstructing the non-linear frequency regulation capacity index based on the frequency regulation parameters in the adjustable resource parameters for the current year to transform the medium- and long-term system regulation capacity evaluation model into a linear form, and obtaining the demand distribution of the medium- and long-term regulation capacity of the power system considering the frequency regulation capacity after solution; The system includes: a data processing module, an index and quantification model establishment module, an evaluation model construction module, and an evaluation model solution 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 medium- and long-term regulation capacity of a power system. Background Art

[0002] With the continuous increase in the proportion of volatile and intermittent renewable power generation resources, it has brought significant challenges to the power system to maintain the balance between supply and demand, and the risks of load loss and curtailment of electricity have increased. At the same time, the decline in the system frequency regulation capacity caused by the replacement of traditional units by a large number of renewable generator sets with converter interfaces also affects the frequency stability of the system under disturbances. In recent years, several large-scale frequency drops have occurred in new energy power grids around the world, resulting in a large number of load curtailments. It can be seen that the regulation capacity of the existing flexible adjustable resources in the system is insufficient to cope with the high-proportion new energy power system, and the system regulation capacity will continue to decline with the increase in the proportion of new energy. Therefore, reasonably quantifying and evaluating the system regulation capacity demand is the basis for enhancing the flexible regulation capacity of the future power system and ensuring the stable operation of the system.

[0003] Currently, the research on the quantitative evaluation of the system flexible regulation capacity mainly focuses on the load and new energy levels that the existing resources in a specific operating section or within the day-ahead scale of the system can cope with, that is, quantifying the available regulation boundaries of the existing adjustable resources. However, by means of load shedding and curtailment of electricity, the quantification of the system regulation capacity demand during the period when the net load exceeds the regulation boundaries of the existing adjustable units is ignored. And in the medium- and long-term scenarios such as unit expansion planning, there are usually multiple operating scenarios where the system demand exceeds the boundaries of the existing units. Therefore, there is still a lack of a clear quantification method for effectively evaluating the system regulation capacity demand that exceeds the regulation boundaries of the existing adjustable units in the medium- and long-term scenarios. In addition, another major drawback of the existing work is the neglect of the quantification of the system frequency regulation capacity. Only a few studies use the traditional spinning reserve coefficient model to determine the total reserve of the system, but the frequency dynamic response process of the system under disturbances is ignored. This method may overestimate the system frequency regulation capacity and result in poor frequency anti-disturbance performance 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 capacity of a power system, which is used to at least solve the problems in the prior art that the power system still lacks a method for evaluating the medium- and long-term regulation capacity requirements and cannot quantify the precise frequency regulation requirements 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 capacity of a power system in medium- and long-term scenarios such as future power system planning scenarios, comprehensively considering the restrictions of the spatio-temporal coupling operation characteristics of the power system, constructing multi-dimensional quantitative indicators for the medium- and long-term system regulation requirements and corresponding quantitative evaluation methods, and the system frequency regulation capacity requirements under the envisioned disturbances can be intuitively quantified by the indicators in the power form of the present invention. The present invention can help grid operators identify the detailed distribution of the system's multi-dimensional regulation capacity deficits and the corresponding frequency regulation effects under various future operation scenarios, and then assist in guiding the grid to determine a reasonable frequency resource planning layout and operation plan, providing support for the stable operation of the power system.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for evaluating the medium- and long-term regulation capacity of a power system, comprising the following steps:

[0007] S1. Obtain the adjustable resource parameters for the current year and the source-load annual prediction data. The source-load annual prediction data is generated by the k-means clustering algorithm to form medium- and long-term typical scenarios as the input data for the system medium- and long-term regulation capacity evaluation model;

[0008] S2. According to the adjustable resource parameters for the current year, establish a multi-dimensional index model for quantifying the medium- and long-term regulation capacity of the system; among them, the multi-dimensional indexes include the conventional regulation capacity index for ensuring the balance between supply and demand and the post-disturbance frequency regulation capacity index considering the system frequency dynamics ;

[0009] S3. Construct a system medium- and long-term regulation capacity evaluation model embedded with a system frequency modulation amplitude index model, with the minimum quantization values of the conventional regulation capacity index and the post-disturbance frequency regulation capacity index that fully meet the medium- and long-term system requirements as the evaluation objective;

[0010] S4. Based on the frequency regulation parameters in the adjustable resource parameters for the current year, combine the data-driven method to reconstruct the non-linear frequency regulation capacity index to transform the system medium- and long-term regulation capacity evaluation model into a linear form, and after solving, evaluate and obtain the distribution of the medium- and long-term regulation capacity requirements of the power system considering the frequency regulation capacity.

[0011] Preferably, the adjustable resource parameters in S1 are the unit technical parameters of the adjustable resources under the conventional grid connection strategy, where the unit technical parameters of the adjustable resources under the conventional grid connection strategy include, but are not limited to: the 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 expected installed capacity of new energy units, the output of new energy units, and the load demand in any future planned year.

[0012] Preferably, in S2, the conventional regulation ability index at the system level Specifically includes the upward and downward regulation amplitudes and as well as the upward and downward regulation speeds at the system level and , and the post-disturbance frequency regulation ability index at the system level includes the frequency regulation amplitude index at the system level :

[0013] ;

[0014] ;

[0015] ;

[0016] In the formula, , , and are all the conventional regulation ability indexes at the node level, and respectively represent the upward and downward regulation amplitudes of the adjustable resource at node at time and respectively represent the upward and downward regulation speeds of the adjustable resource at node at time

[0017] is the frequency regulation amplitude index at the node level, that is, the frequency regulation amplitude index of the adjustable resource at node at time, which is used to quantify the additional maximum frequency regulation power required to support the system to achieve the frequency regulation effect under the presupposed disturbance.

[0018] Preferably, the long-term regulation ability evaluation model of the system in S3 is specifically:

[0019] ;

[0020] The constraints include: constraints in the normal operating state and after a fault;

[0021] The constraints in the normal operating state include a quantitative model of the normal regulation ability index at the node level;

[0022] The constraints after a fault include: a quantitative model of the frequency regulation amplitude index at the node level considering the frequency dynamics , incorporating the system equivalent frequency regulation parameter model, and the constraint conditions of the frequency regulation effect index incorporating where the frequency regulation effect index includes the initial moment frequency change rate , the extreme value of frequency deviation and the quasi-steady state frequency .

[0023] Preferably, the quantitative model of the normal regulation ability index at the node level is:

[0024] ;

[0025] where is the collaborative output of the system demand level and inventory resources at time , is the line transmission power at time ; represents the system operation limit equation, represents the adjustable unit operation characteristic model incorporating the normal regulation ability index, represents other operation characteristics of the adjustable resources, is the system operation limit model jointly composed of system network security, load demand, and non-adjustable new energy unit restrictions.

[0026] Preferably, based on thermal power units, the quantitative model of the frequency regulation amplitude index at the node level is:

[0027] ;

[0028] where is the maximum value of the unit response power during the transient process of the system frequency response; is the start-stop state of the thermal power unit at node at time ; is the maximum output limit of the thermal power unit at node ;

[0029] The system equivalent frequency regulation parameter model incorporating is:

[0030] ;

[0031] ;

[0032] In the formula, and are both system equivalent frequency regulation parameters incorporated into , representing the system equivalent inertia and governor droop coefficient at the moment in the medium- and long-term scenario respectively, is the base power, is the set of thermal power units in the system, is the node where the thermal power unit is located, is the node where the thermal power unit is located, and

[0033] is the initial moment frequency change rate , the extreme value of frequency deviation and the quasi-steady state frequency are respectively subject to the following constraints:

[0034] ;

[0035] ;

[0036] ;

[0037] Among them, is the active power disturbance of the system at the moment, is 's maximum safety limit, is the system load damping coefficient, , , are respectively intermediate parameters that couple the non-linear relationship of the system equivalent frequency regulation parameters, is the time when the system frequency deviation reaches the lowest point, is the safety limit of the frequency lowest point index, is the safety limit of the quasi-steady state frequency index.

[0038] Preferably, the specific content of obtaining the extreme value of frequency deviation includes:

[0039] The system frequency dynamics is described by the swing equation when the system is disturbed. In the case where thermal power units are the main adjustable units, the system frequency dynamic response model is:

[0040] ;

[0041] Among them, is the system frequency deviation at time is the load damping coefficient, is at time the active power adjustment amount of the thermal power unit for primary frequency regulation at node

[0042] Considering the prime mover-governor of the synchronous unit and the droop control link of the virtual synchronous machine, the expression of the primary frequency regulation adjustment power in the Laplace domain is:

[0043] ;

[0044] Among them, is the turbine coefficient of the synchronous unit turbine at node is the reheater time constant of the synchronous unit at node ;

[0045] Furthermore, the equivalent frequency dynamic analysis model of the system frequency dynamics is obtained as:

[0046] ;

[0047] ;

[0048] Among them, is the system frequency deviation, is the system load damping coefficient;

[0049] According to the equivalent frequency dynamic analysis model of the system frequency dynamics, obtain the extreme value of the frequency deviation corresponding to the moment :

[0050] .

[0051] Preferably, the specific content of S4 includes:

[0052] (1) Combining the frequency modulation parameters in the adjustable resource parameters of the current year and the quantifiable upper limit of the frequency modulation amplitude index, obtain the original range of the system equivalent frequency modulation parameters: and Among them, and are the upper and lower limits of the system equivalent frequency modulation parameter and respectively. Sample to generate sample points and ​, and calculate based on the equivalent frequency dynamic parsing model , to generate a sample point surface; wherein the frequency modulation parameters in the adjustable resource parameters of the current year include, but are not limited to: installed capacity and inertia time constant, droop coefficient and response time constant of the governor;

[0053] (2) Obtain the sample point range within the range that satisfies through the data-driven classification method, and then tighten the original range, and then replace the original range with the tightened range and , where and are respectively and the tightened values;

[0054] (3) Reconstruct using the linearization method of linear approximation ;

[0055] Convert the quantization model of into a linear combination form that is easy to incorporate into the system equivalent frequency modulation parameters and :

[0056] ;

[0057] Wherein, is the approximation error corresponding to the th sample point, is the tightened range; Ensure that the approximate linear result is above the formed observation surface, and are respectively the linear fitting coefficients;

[0058] Then the non-linear frequency deviation extreme value is finally reconstructed into a linear form model representing the boundary range of the frequency modulation parameters:

[0059] ;

[0060] Replace the original non-linear frequency deviation extreme value with the reconstructed linear form model to obtain the final long-term and medium-term regulation capacity evaluation model of the system, and complete the solution of the final long-term and medium-term regulation capacity evaluation model of the system.

[0061] A long-term and medium-term regulation capacity evaluation system for a power system, comprising:

[0062] The data processing module is used to obtain the current year's adjustable resource parameters and the annual source-load forecast data. The annual source-load forecast data is used to generate medium- and long-term typical scenarios through the k-means clustering algorithm as input data for the system's medium- and long-term regulation capacity assessment model;

[0063] 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 ;

[0064] 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;

[0065] 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 a data-driven method to transform the system's medium- and long-term regulation capability evaluation model into a linear form. After solution, the medium- and long-term regulation capability demand distribution of the power system considering the frequency regulation capability is obtained.

[0066] As can be seen from the above technical solutions, 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:

[0067] The present invention comprehensively considers the limitations of the spatiotemporal coupled operating characteristics of the power system, constructs a multi-dimensional indicator and corresponding quantitative evaluation method that can clearly quantify the medium- and long-term system regulation needs, and can accurately quantify the frequency regulation capacity requirements during the dynamic process of the system frequency response. The system frequency regulation capacity requirements under expected disturbances can be intuitively quantified and presented through the power-form indicator of the present invention, solving the problem that the current power system cannot accurately quantify the regulation capacity requirements 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 operating scenarios, and then assist in guiding the power grid to determine a reasonable multi-type regulation resource planning layout and operation plan. It can effectively avoid the orderly load shedding caused by the imbalance of supply and demand after the large-scale new energy grid is connected in the future, the curtailment of new energy power, and the low-frequency load reduction caused by the insufficient system frequency regulation capacity under short-term high-power shocks. It provides strong support for the stable operation of the power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0069] Figure 1 Schematic diagram of the process for the medium - and long - term regulation capacity assessment method of the power system provided by the present invention;

[0070] Figure 2 Topological diagram of the test system provided by the embodiments of the present invention;

[0071] Figure 3 Time distribution diagram of the quantization results of the multi - dimensional indicators of the medium - and long - term regulation capacity of the system provided by the embodiments of the present invention; Figure 3 (a) is 、 and 's time distribution diagram; Figure 3 (b) is and 's time distribution diagram; Figure 3 (c) is 、 、 、 、 and 's time distribution diagram;

[0072] [[ID=4,]] Figure 4 Comparison diagram of the quantization results of the system frequency modulation amplitude index provided by the embodiments of the present invention;

[0073] Figure 5 Comparison diagram of the frequency simulation results provided by the embodiments of the present invention. Detailed implementation manners

[0074] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0075] The present invention provides a method for assessing the medium - and long - term regulation capacity of a power system. As Figure 1 shown, it includes the following steps:

[0076] S1. Obtain the adjustable resource parameters for the current year and the annual source-load forecast data. The annual source-load forecast data generates medium- and long-term typical scenarios through the k-means clustering algorithm as the input data for the medium- and long-term regulation capacity evaluation model of the system.

[0077] S2. Based on the adjustable resource parameters for the current year, establish a multi-dimensional index model for quantifying the medium- and long-term regulation capacity of the system. Among them, the multi-dimensional indexes include the conventional regulation capacity index for ensuring the balance between supply and demand and the post-disturbance frequency regulation capacity index considering the system frequency dynamics ;

[0078] S3. Construct a medium- and long-term regulation capacity evaluation model of the system embedded with a system frequency modulation amplitude index model, with the minimum quantization values of the conventional regulation capacity index and the post-disturbance frequency regulation capacity index that fully meet the medium- and long-term system requirements as the evaluation objective;

[0079] S4. Based on the frequency modulation parameters in the adjustable resource parameters for the current year, combine the data-driven method to reconstruct the non-linear frequency modulation capacity index to transform the medium- and long-term regulation capacity evaluation model of the system into a linear form, and after solving, evaluate and obtain the distribution of the medium- and long-term regulation capacity requirements of the power system considering the frequency modulation capacity.

[0080] To further implement the above technical solution, the adjustable resource parameters in S1 are the unit technical parameters of the adjustable resources under the conventional grid connection strategy, and the unit technical parameters of the adjustable resources under the conventional grid connection strategy include, but are not limited to: the installed capacity, ramping, minimum technical output, and frequency modulation parameters of various adjustable resources; the annual source-load forecast data includes the annual forecast data of the expected installed capacity, output of new energy units, and load demand in any future planned year.

[0081] It should be noted that:

[0082] The adjustable resource parameters for the current year are the unit technical parameters such as the installed capacity, ramping, minimum technical output, and frequency modulation parameters of various adjustable resources such as thermal power, hydropower, and energy storage except for the non-adjustable wind and light units under the conventional grid connection strategy.

[0083] To further implement the above technical solution, in S2, the conventional regulation capacity index at the system level specifically includes the upward and downward regulation amplitudes and and the upward and downward regulation speeds at the system level and ; the post-disturbance frequency regulation capacity index at the system level includes the frequency modulation amplitude index at the system level :

[0084] ;

[0085] ;

[0086] ;

[0087] In the formula, , , and are all conventional regulation ability indicators at the node level. and respectively represent the upward and downward regulation ranges of the adjustable resource at node at time and respectively represent the upward and downward regulation speeds of the adjustable resource at node at time

[0088] is the frequency modulation amplitude indicator at the node level, that is, the frequency modulation amplitude indicator of the adjustable resource at node at time, which 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.

[0089] It should be noted that:

[0090] In order to fully consider the spatio-temporal coupling characteristics of the system regulation ability to ensure the feasibility of the quantified system regulation ability, the total power that needs to be additionally injected or withdrawn to meet the regulation requirements of the system during the period of insufficient regulation ability is decomposed from two aspects of amplitude and speed. Furthermore, the conventional regulation ability indicators include the upward and downward regulation ranges of the adjustable resource at node and the upward and downward regulation speeds .

[0091] To further implement the above technical solution, the long-term regulation ability evaluation model of the system in S3 is specifically:

[0092] ;

[0093] The constraint conditions include: constraints in the normal operation state and the post-fault state;

[0094] The constraints in the normal operation state include the quantification model of the conventional regulation ability indicators at the node level;

[0095] Constraints in the post-fault state include: the frequency regulation amplitude index at the node level considering frequency dynamics quantification model, incorporating system equivalent frequency regulation parameter model, and incorporating constraint conditions of the frequency regulation effect index after , where the frequency regulation effect index includes the initial moment frequency change rate , the extreme value of frequency deviation .

[0096] To further implement the above technical solution, the quantification model of the conventional regulation ability index at the node level is:

[0097] ;

[0098] where is the coordinated output of the system demand level and stock resources at time is the line transmission power at time represents the system operation limit equation, represents the adjustable unit operation characteristic model incorporating the conventional regulation ability index, represents other operation characteristics of the adjustable resources, is the system operation limit model jointly composed of system network security, load demand, and non-adjustable new energy unit restrictions.

[0099] It should be noted that:

[0100] Both the adjustable unit operation characteristics and the system operation limit model are modeled using general models. Among them, the system operation limit includes system network power flow and non-adjustable new energy unit and load demand restrictions. The adjustable units include, but are not limited to, thermal power units, hydropower units, and other adjustable units such as energy storage. The specific type of adjustable unit depends on the combination of adjustable unit types in the system to be evaluated in the current year. In this embodiment, thermal power units are taken as an example for illustration, and its model after incorporating the regulation ability index is:

[0101] ;

[0102] ;

[0103] ;

[0104] ;

[0105] where is the thermal power unit at node at Output at For thermal power units Output limit and up / down ramping For node Thermal power units at At Start / stop state at

[0106] Other operating characteristic models of thermal power units Include start / stop characteristic models representing their unit commitment, specifically:

[0107] ;

[0108] ;

[0109] ;

[0110] ;

[0111] ;

[0112] Among them, For node Thermal power units at Start / stop action of Is the minimum start / stop time of the thermal power unit.

[0113] In order to further implement the above technical solution, the frequency modulation amplitude index quantifies the additional maximum frequency modulation power required for the support system to achieve the frequency modulation effect under the anticipated disturbance. In order to accurately quantify the frequency modulation ability in the post-fault state of the system, the quantification process of the frequency modulation amplitude index All need to consider the system frequency dynamic process, and incorporate the node frequency modulation amplitude parameter Based on the node where the thermal power unit is located. It quantifies the power deficit required for the units participating in frequency modulation to have sufficient frequency modulation response power, that is, after incorporating The remaining power processing can just meet the maximum frequency modulation response power. Based on the thermal power unit, the quantification model of the frequency modulation amplitude index at the node level Is:

[0114] ;

[0115] Among them, Is the maximum value of the unit response power During the transient process of the system frequency response, estimated through the system quasi-steady state frequency and the governor droop coefficient Of the thermal power unit at node ; For estimation; For node Thermal power units at Start-stop status at ; For node the maximum output limit of the thermal power unit at;

[0116] The system equivalent frequency regulation parameter model incorporated is: ;

[0117] ;

[0118] ;

[0119] In the formula, and are both system equivalent frequency regulation parameters incorporated, representing the system equivalent inertia and governor droop coefficient at the [[ID=3o]] moment in the medium- and long-term scenario respectively, is the base power, is the set of system thermal power units, is the inertia time constant of the thermal power unit at node ; is the droop coefficient of the governor of the thermal power unit at node ; ;

[0120] The constraint conditions for the initial moment frequency change rate , frequency deviation extreme value and quasi-steady state frequency are respectively:

[0121] ;

[0122] ;

[0123] ,

[0124] Among them, is the active power disturbance of the system at the moment, is the maximum safety limit of , is the system load damping coefficient, are intermediate parameters coupling the non-linear relationship of system equivalent frequency regulation parameters respectively, is the time when the system frequency deviation reaches the lowest point, is the safety limit of the frequency lowest point index, is the safety limit of the quasi-steady state frequency index.

[0125] To further implement the above technical solution and obtain the extreme value of frequency deviation The specific content includes:

[0126] The dynamic of the system frequency is described by the swing equation when the system is disturbed. In the case where thermal power units are the main adjustable units, the system frequency dynamic response model is:

[0127] ;

[0128] Wherein, is the system frequency deviation at is the load damping coefficient, is the active power adjustment amount of the primary frequency regulation of the thermal power unit at node at ;

[0129] Considering the prime mover-governor of the synchronous unit and the droop control link of the virtual synchronous machine, the expression of the primary frequency regulation adjustment power in the Laplace domain is:

[0130] ;

[0131] Wherein, is the turbine coefficient of the turbine of the synchronous unit at node and is the reheater time constant of the synchronous unit at node ;

[0132] Furthermore, the equivalent frequency dynamic analysis model of the system frequency dynamic is obtained as:

[0133] ;

[0134] ;

[0135] Wherein, is the system frequency deviation, is the system load damping coefficient;

[0136] According to the equivalent frequency dynamic analysis model of the system frequency dynamic, obtain the extreme value of the frequency deviation corresponding to the moment :

[0137] .

[0138] To further implement the above technical solution, the specific content of S4 includes:

[0139] (1)Combine the frequency modulation parameter in the adjustable resource parameters of the current year and the quantifiable upper limit of the frequency modulation amplitude index to obtain the original range of the system equivalent frequency modulation parameter: and , where and are the upper and lower limits of the system equivalent frequency modulation parameter and respectively. Sample points and are generated within the original range, and is calculated based on the equivalent frequency dynamic analysis model to generate a sample point surface; the frequency modulation parameter in the adjustable resource parameters of the current year includes, but is not limited to: installed capacity and inertia time constant, droop coefficient and response time constant of the governor;

[0140] (2)Based on the data-driven classification method, obtain the sample point range within the range of to further tighten the original range, and then replace the original range with the tightened range and , where and are and respectively after tightening;

[0141] (3)Reconstruct using the linearization method of linear approximation;

[0142] Convert the quantization model of into a linear combination form that is easy to incorporate into the system equivalent frequency modulation parameter and :

[0143] ;

[0144] where is the approximation error corresponding to the th sample point, and is the tightened range; Ensure that the approximate linear result is above the formed observation surface, and and are the linear fitting coefficients respectively;

[0145] Then the non-linear frequency deviation extreme value is finally reconstructed into a linear form model representing the boundary range of the frequency modulation parameter:

[0146] ;

[0147] Replace the original non-linear frequency deviation extreme value with the reconstructed linear form model.Obtain the long-term regulation capacity evaluation model of the final system, and complete the solution of the long-term regulation capacity evaluation model of the final system.

[0148] It should be noted that:

[0149] Due to the fact that the model of the frequency deviation extreme value index is a highly non-linear coupling relationship regarding the equivalent frequency regulation parameters of the system, resulting in the unsolvability of the evaluation model. Therefore, the above method is proposed to reconstruct the non-linear part and then solve the evaluation model.

[0150] A long-term regulation capacity evaluation system for a power system, including:

[0151] A data processing module, used to obtain the adjustable resource parameters of the current year 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 long-term regulation capacity evaluation model of the system;

[0152] An index and quantization model establishment module, used to establish a multi-dimensional index model for quantifying the long-term regulation capacity of the system according to the adjustable resource parameters of the current year; among them, the multi-dimensional indexes include the conventional regulation capacity index to ensure the balance of supply and demand and the post-disturbance frequency regulation capacity index considering the system frequency dynamics ;

[0153] An evaluation model construction module, used to construct a long-term regulation capacity evaluation model of the system embedded with a system frequency modulation amplitude index model, with the quantization values of the conventional regulation capacity index and the post-disturbance frequency regulation capacity index that fully meet the medium- and long-term system requirements being minimized as the evaluation objective;

[0154] An evaluation model solution module, used to reconstruct the non-linear frequency regulation capacity index based on the frequency regulation parameters in the adjustable resource parameters of the current year in combination with the data-driven method to transform the long-term regulation capacity evaluation model of the system into a linear form, and after solution, evaluate and obtain the long-term regulation capacity demand distribution of the power system considering the frequency regulation capacity.

[0155] The present invention will be further described through experiments below:

[0156] Take the evaluation of the annual frequency modulation amplitude index of the inventory resources of the improved IEEE HRP-38 system in the future planning scenario as an example for illustration.

[0157] Figure 2It is a test system topology diagram. The total capacities of the synchronous units, WT, and PV in the system are modified to 140.14 GW, 60.72 GW, and 101.14 GW respectively, and the proportion of new energy exceeds 50%. The annual source-load prediction data generates 8 typical days through the K-means clustering method. The boundaries of the frequency modulation amplitude index and are 5500 MW and 3000 MW respectively, and the boundaries of the frequency modulation effect , and are set to 0.5 Hz / s, 0.5 Hz, and 0.3 Hz.

[0158] 1. Medium- and long-term regulation ability assessment results

[0159] Based on the regulation ability assessment method of the present invention, the medium- and long-term regulation ability assessment results of the HRP-38 system are obtained, and the time distribution of the multi-dimensional indexes of the system is as Figure 3 shown. In typical days 1, 6, and 8, which represent higher system demands, the upward regulation amplitude of the system is insufficient in most periods, indicating that the existing resources cannot meet the load demand. There is no shortage in most periods of other scenarios with relatively low load levels. Only in the 19-23h and 21-24h of high load demands on typical days 4 and 5, which represent the medium load level throughout the year, there are small quantification values. The frequency modulation amplitude is insufficient during the high load periods in all scenarios. Since the system frequency modulation ability during this period is entirely borne by , therefore the quantification value is relatively large. While during this period (such as 20-22h on typical day 2), the regulation ability of the existing resources can ensure the system demand and there is some available frequency modulation amplitude, but it is not enough to resist the expected disturbances. Therefore, the quantification value represents the system frequency modulation ability demand in addition to the available frequency modulation ability of the existing resources. And at 15h and 16h on typical day 7, which represents the minimum system demand throughout the year, the downward regulation amplitude of the system is insufficient.

[0160] Regarding the system regulation speed index, there are demand quantification values respectively in some typical scenarios where the load reduction (increase) speed is the fastest, indicating that the rapid change of the system demand during these periods exceeds the regulation speed of the existing resources. Figure 3 (c) shows the system frequency modulation effect index for a specific typical scenario. It can be seen that after incorporating the system frequency modulation demand quantification value, the frequency modulation effect of each period of the system is ensured within the limit.

[0161] 2. Validation of the Effectiveness of the System Frequency Regulation Capacity Quantification Method

[0162] To further verify the effectiveness of the frequency regulation capacity quantification method in the system regulation capacity evaluation method proposed by the present invention and the necessity of considering frequency dynamics and frequency regulation capacity quantification, the following three methods are respectively set for comparison:

[0163] Proposed method: The frequency regulation capacity quantification method proposed by the present invention;

[0164] Traditional method 1: Without considering the system frequency dynamic model, the traditional spinning reserve model is used to quantify the system frequency regulation amplitude , and the reserve coefficient is set to 5% of the load;

[0165] Traditional method 2: Without considering the system regulation capacity evaluation of the system frequency regulation capacity, the system frequency dynamics and frequency regulation capacity quantification model are ignored.

[0166] The quantification results of the system regulation amplitude are as Figure 4 shown. Traditional method 2 without considering the system frequency regulation capacity has no frequency regulation capacity quantification. Since traditional method 1 does not consider frequency dynamics, the obtained in Case 2 with a reserve coefficient of only 5% of the load is less than the proposed method. The evaluation results at a certain moment are selected for system frequency dynamic simulation to further verify the frequency regulation effect of the proposed frequency regulation capacity quantification method and the necessity of considering frequency dynamics. The simulation results are as Figure 5 shown. The proposed method considering frequency dynamics can quantify the frequency regulation requirements of the units. After the adjustable units are incorporated into the quantification results, they all have sufficient frequency regulation capacity. Therefore, all adjustable units participate in the system frequency response, and their response power is within the quantified limits. The simulation also shows that the proposed quantification method effectively guarantees the system frequency safety. For traditional method 2 without considering the system frequency regulation capacity quantification, the adjustable units have no frequency regulation amplitude space. Although the frequency regulation capacity requirements of some nodes, such as B2 and B4, are quantified in the traditional method, due to the lack of consideration of the frequency dynamics of the units, the spatial distribution of the frequency regulation amplitude index is unreasonable, and the units at B2 and B4 have no available regulation speed. Therefore, the amplitude limits of the adjustable units of the two traditional methods are both 0 and they cannot participate in primary frequency regulation, only providing inertial response. Neither of them can guarantee the frequency stability of the system under the same disturbance. Thus, it can be seen that the regulation capacity quantification method proposed by the present invention can effectively quantify the shortage of the system frequency regulation capacity, and effectively guarantee the system frequency safety after incorporating the frequency regulation capacity quantification value, providing support for the stable operation of the system.

[0167] 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 foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included within the protection scope of the present application.

Claims

1. A medium- and long-term regulation capacity assessment method for a power system, characterized in that, It includes the following steps: S1. Obtain the adjustable resource parameters for the current year and the annual source-load forecast data. The annual source-load forecast data generates medium- and long-term typical scenarios through the k-means clustering algorithm as the input data for the system's medium- and long-term regulation capacity evaluation model; S2. Establish a multi-dimensional index model for the medium- and long-term regulation capacity of the quantization system according to the adjustable resource parameters in the current year; among them, the multi-dimensional indexes include the conventional regulation capacity index to ensure the balance between supply and demand and the post-disturbance frequency modulation capacity index considering the dynamic system frequency ; the conventional regulation capacity index at the system level specifically includes the upward and downward regulation amplitudes and as well as the upward and downward regulation speeds at the system level and , and the post-disturbance frequency modulation capacity index at the system level includes the frequency modulation amplitude index at the system level ; S3. Build a system medium- and long-term regulation capacity evaluation model embedded with a system frequency regulation amplitude index model, with the minimum quantization value of the conventional regulation capacity index that fully meets the medium- and long-term system requirements and the frequency regulation capacity index after perturbation as the evaluation objective; S4. Based on the frequency regulation parameters in the adjustable resource parameters for the current year, combine the data-driven method to reconstruct the non-linear frequency regulation capacity index to transform the system's medium- and long-term regulation capacity evaluation model into a linear form. After solving, evaluate and obtain the medium- and long-term regulation capacity demand distribution of the power system considering the frequency regulation capacity.

2. The medium- and long-term regulation capacity evaluation method of a power system according to claim 1, wherein In S1, the adjustable resource parameters are the unit technical parameters of the adjustable resources under the conventional grid connection strategy. Among them, the unit technical parameters of the adjustable resources under the conventional grid connection strategy include: the installed capacity, ramping rate, minimum technical output, and frequency regulation parameters of various adjustable resources; the annual source-load forecast data includes the annual forecast data of the expected installed capacity of new energy units, the output of new energy units, and the load demand in any future planned year.

3. The medium- and long-term regulation capacity evaluation method for a power system according to claim 1, characterized in that In S2, the calculation method of the general regulation ability index at the system level and the frequency regulation ability index after disturbance is as follows: ； ； ； In the formula, , , and are all conventional adjustment ability indicators at the node level. and respectively represent the upward and downward adjustment ranges of the adjustable resources at node at time and respectively represent the upward and downward adjustment speeds of the adjustable resources at node at time is the frequency regulation amplitude index at the node level, i.e., the adjustable resources at the node at the frequency regulation amplitude index at the moment, which is used to quantify the additional maximum frequency regulation power required to support the system to achieve the frequency regulation effect under the pre-conceived disturbance.

4. The medium- and long-term regulation capacity assessment method for a power system according to claim 3, wherein The specific system medium- and long-term regulation capacity evaluation model in S3 is: ； The constraint conditions include: constraints in the normal operation state and after a fault; The constraints in the normal operation state include the quantization model of the normal regulation capacity index at the node level; Constraints in the post-fault state include: the frequency regulation amplitude index at the node level considering frequency dynamics quantification model, incorporating system equivalent frequency regulation parameter model, and the constraint conditions of the frequency regulation effect index after incorporating , where the frequency regulation effect index includes the initial moment frequency change rate , the extreme value of frequency deviation and the quasi-steady state frequency .

5. The medium- and long-term regulation capacity evaluation method for a power system according to claim 4, wherein, The quantization model of the normal regulation capacity index at the node level is: ； Among them, is the collaborative output of the system demand level and stock resources at a certain moment, is the line transmission power at a certain moment; represents the system operation limit equation, represents the adjustable unit operation characteristic model incorporating the conventional regulation ability index, represents other operation characteristics of adjustable resources, is the system operation limit model jointly constituted by system network security, load demand and the limits of non-adjustable new energy units.

6. The medium- and long-term regulation capacity evaluation method for a power system according to claim 4, characterized in that, Taking the thermal power unit as the reference, the frequency modulation amplitude index at the node level The quantization model is as follows: ； Among them, is the maximum value of the unit response power during the transient process of the system frequency response; The maximum value; is the node where the thermal power unit at start-stop state; is the node where the thermal power unit maximum output limit; Included The system equivalent frequency modulation parameter model is as follows: ； ； Wherein, and are both system equivalent frequency regulation parameters incorporated into , respectively representing the system equivalent inertia and governor droop coefficient at time in the medium- and long-term scenario, is the base power, is the set of thermal power units in the system, is the node where the thermal power unit is located, and is the inertia time constant of the thermal power unit at the node ; the droop coefficient of the governor Initial moment frequency change rate , extreme value of frequency deviation and quasi-steady state frequency The constraint conditions are respectively as follows: ； ； ； Among them, is the active power disturbance at time of the system, is the maximum safety limit of is the system load damping coefficient, , , are intermediate parameters that couple the nonlinear relationship of the system equivalent frequency modulation parameters respectively, is the time when the system frequency deviation reaches the lowest point, is the safety limit of the lowest frequency point index, is the safety limit of the quasi-steady state frequency index.

7. A medium- and long-term regulation capacity evaluation method for a power system according to claim 6, characterized in that, Obtain the extreme value of frequency deviation The specific content includes: The system frequency dynamics is described by the swing equation when the system is disturbed. In the case where thermal power units are the main adjustable units, the system frequency dynamic response model is: ； Among them, is the system frequency deviation at this time, is the load damping coefficient, is the active power adjustment amount of the primary frequency regulation of the thermal power unit at node at this time; The first frequency modulation active power adjustment amount; Considering the prime mover-governor of the synchronous unit and the droop control link of the virtual synchronous machine, the power adjusted by primary frequency modulation has the following Laplace-domain expression: ； Among them, is the turbine coefficient of the synchronous unit turbine at the node , and is the reheater time constant of the synchronous unit at the node . Furthermore, the equivalent frequency dynamic analysis model of the system frequency dynamics is obtained as: ； ； Among them, is the system frequency deviation, is the system load damping coefficient; Obtained according to the equivalent frequency dynamic analysis model that varies with the system frequency The extreme value of the frequency deviation corresponding to the moment : 。 8. A method for evaluating the medium- and long-term regulation ability of a power system according to claim 6, characterized in that, The specific content of S4 includes: (1) Combine the frequency regulation parameter in the adjustable resource parameters of the current year and the quantifiable upper limit of the frequency regulation amplitude index to obtain the original range of the system equivalent frequency regulation parameter: and , where and are the upper and lower limits of the system equivalent frequency regulation parameter and respectively. Sample to generate sample points and within the original range, and calculate based on the equivalent frequency dynamic analysis model to generate a sample point surface; the frequency regulation parameter in the adjustable resource parameters of the current year includes: installed capacity and inertia time constant, droop coefficient and response time constant of the governor; (2) Obtain the sample point range within through the data-driven classification method, and then tighten the original range. Subsequently, replace the original range with the tightened range and , where and are respectively and the tightened values; (3) Reconstruct using a linearization method of linear approximation ; Convert the quantization model into a form that is easy to incorporate into the system equivalent frequency modulation parameter and in the form of a linear combination: ； Among them, is the approximation error corresponding to the th sample point, is the range after contraction; Ensure that the approximate linear result is above the constructed observation surface, and are the linear fitting coefficients respectively; The extreme value of the non-linear frequency deviation Finally, it is reconstructed into a linear form model representing the boundary range of the frequency modulation parameter: ； Replace the original non-linear frequency deviation extreme value with the reconstructed linear form model Obtain the final medium- and long-term regulation capacity evaluation model of the system, and complete the solution of the final medium- and long-term regulation capacity evaluation model of the system.

9. A medium- and long-term regulation capacity evaluation system for a power system, characterized in that It includes: A data processing module, which is used to obtain the adjustable resource parameters for the current year and the annual source-load forecast data. The annual source-load forecast data generates medium- and long-term typical scenarios through the k-means clustering algorithm as the input data for the system's medium- and long-term regulation capacity evaluation model; An index and quantitative model establishment module, which is used to establish a multi-dimensional index model for the medium- and long-term regulation ability of the quantitative system according to the adjustable resource parameters in the current year; the multi-dimensional indexes include the conventional regulation ability index to ensure the balance between supply and demand and the post-disturbance frequency regulation ability index considering the system frequency dynamics ; the conventional regulation ability index at the system level Specifically includes the upward and downward regulation amplitudes and as well as the upward and downward regulation speeds at the system level and , the post-disturbance frequency regulation ability index at the system level includes the frequency regulation amplitude index at the system level ; An evaluation model construction module is used to construct a medium- and long-term system regulation capacity evaluation model embedded with a system frequency modulation amplitude index model, with the conventional regulation capacity index that fully meets the medium- and long-term system requirements and the frequency modulation capacity index after perturbation The minimum quantization value is taken as the evaluation objective; An evaluation model solving module, which is used to reconstruct the non-linear frequency regulation capacity index based on the frequency regulation parameters in the adjustable resource parameters for the current year, combine the data-driven method to transform the system's medium- and long-term regulation capacity evaluation model into a linear form, and evaluate and obtain the medium- and long-term regulation capacity demand distribution of the power system considering the frequency regulation capacity after solving.

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