Method for evaluating primary frequency modulation demand of new energy power system under deep peak regulation working condition

Abstract

This invention discloses a method for assessing the primary frequency regulation demand of a new energy power system under deep peak shaving conditions, comprising: obtaining the capacity proportion of deep-shaving units; calculating the actual frequency regulation coefficient of each deep-shaving unit based on the frequency regulation mechanism of thermal power units; obtaining the system frequency regulation coefficient based on the capacity proportion of deep-shaving units, the actual frequency regulation coefficient, and the average frequency regulation coefficient of non-deep-shaving units; obtaining the system frequency response function based on the system frequency regulation coefficient; obtaining the time of the lowest frequency point and the maximum frequency deviation based on the system frequency response function; if the maximum frequency deviation exceeds a preset range, determining that there is a risk of frequency exceeding the limit; obtaining the minimum system frequency regulation coefficient by combining the system frequency response function, the time of the lowest frequency point, and the maximum frequency deviation with linear simplification; and obtaining the limit of the deep-shaving ratio boundary based on the minimum system frequency regulation coefficient and the system frequency regulation coefficient.

Description

Technical Field

[0001] This invention relates to the field of power electronics technology, and in particular to a method for assessing the primary frequency regulation demand of a new energy power system under deep peak shaving conditions. Background Technology

[0002] With the large-scale integration of renewable energy sources, such as wind and solar power, into the power grid, the power system's power structure, operating characteristics, and stability are undergoing profound changes. The randomness, volatility, and intermittency of renewable energy output not only significantly increase the net load peak-to-valley difference but also pose a severe challenge to frequency stability. Against this backdrop, synchronous thermal power units need to frequently enter deep peak-shaving operation (typically referring to 30% to 40% of rated load) to promote renewable energy absorption and smooth net load fluctuations. However, under deep peak-shaving conditions, the primary frequency regulation capability of thermal power units, which provides active power support, will significantly decrease due to changes in boiler thermal system operating parameters.

[0003] Insufficient primary frequency regulation capability of thermal power units is a significant factor inducing system frequency instability. The reduced primary frequency regulation capability of thermal power units due to deep peak shaving worsens system frequency stability characteristics. Current new power systems face a profound contradiction between "promoting consumption" and "ensuring safety": the need to absorb new energy sources forces large-scale units to implement deep frequency regulation, directly weakening the system's frequency regulation margin to cope with large disturbances. Although existing research has explored the relationship between frequency regulation capability and system safety, most of these studies have not considered the weakening effect of deep peak shaving on the primary frequency regulation characteristics of units and system frequency safety from a physical mechanism perspective.

[0004] At the individual unit level, existing research has explored the physical mechanisms and models underlying the attenuation of primary frequency regulation capability under deep peak shaving conditions. Boiler thermal storage dynamics are crucial in determining the speed and capacity of primary frequency regulation response; the essence of primary frequency regulation is the rapid release of boiler thermal storage to alter prime mover output. Based on this, scholars have established a low-order dynamic model of the boiler-turbine system, incorporating key parameters such as the equivalent thermal storage coefficient, initial main steam pressure, and initial valve opening, through mechanistic analysis and simplification. This model reveals the reasons for the decline in primary frequency regulation capability caused by deep peak shaving: low-load operation reduces the boiler's thermal storage base, weakening the material basis for frequency regulation capability; furthermore, it significantly reduces the incremental steam flow that can be released with the same valve opening adjustment. These studies have successfully characterized the dynamic attenuation of the frequency regulation capability coefficient of a single unit under varying operating conditions. However, most existing research stops at establishing single-unit models, identifying parameters, or analyzing characteristics, failing to integrate them into a system-level frequency response analysis framework. Therefore, it is impossible to assess the impact of high-proportion deep-shaving units on the overall frequency security risk of the power grid, and it is also difficult to guide the safety decision-making regarding the proportion of deep-shaving units in actual dispatching.

[0005] At the system level, existing research on frequency security and primary frequency regulation demand assessment focuses on developing fast and accurate analytical methods for frequency dynamics to assess the minimum frequency point under large disturbances and inversely deduce the minimum primary frequency regulation requirement needed to meet safety constraints. These methods construct analytical relationships between system inertia, frequency regulation capability, and frequency dynamics. However, existing methods generally rely on an idealized assumption: that all synchronous generators in the system operate at rated or higher load conditions, and their primary frequency regulation capability can be uniformly characterized by a constant droop coefficient. This assumption ignores the phenomenon that some generators, operating under deep droop conditions, have significantly reduced actual frequency regulation capability. Therefore, assessment results based on this assumption are often overly optimistic and fail to accurately reflect the significant gap between the actual effective supply and real demand of primary frequency regulation resources in this new typical operating scenario where a high proportion of renewable energy and a high proportion of deep peak-shaving generators coexist.

[0006] In summary, for power systems with a high proportion of deep-peak-shaving units, the existing assessment system lacks a quantitative assessment framework that can organically connect the physical state of the underlying units with the frequency security boundary of the top-level system, making it difficult for the assessment results to provide a reliable basis for dispatch decisions. Therefore, a new technical solution is urgently needed to address the technical problem of how to conduct analytical assessment of the system's primary frequency regulation demand and security boundary while taking into account deep peak-shaving constraints. Summary of the Invention

[0007] This invention provides a method for assessing the primary frequency regulation demand of a new energy power system under deep peak shaving conditions, in order to solve the technical problem of how to perform analytical assessment of the primary frequency regulation demand and safety boundary of the system while taking into account the constraints of deep peak shaving.

[0008] To achieve the above objectives, this invention provides a method for assessing the primary frequency regulation demand of a renewable energy power system under deep peak-shaving conditions, comprising: Obtain the capacity ratio of deep-regulation units; calculate the actual frequency regulation coefficient of each deep-regulation unit based on the frequency regulation mechanism of thermal power units; obtain the system frequency regulation coefficient based on the capacity ratio of deep-regulation units, the actual frequency regulation coefficient, and the average frequency regulation coefficient of non-deep-regulation units. The system frequency response function is obtained based on the system frequency modulation coefficient; the time of the lowest frequency and the maximum frequency deviation are obtained based on the system frequency response function; if the maximum frequency deviation exceeds the preset range, it is determined that there is a risk of frequency exceeding the limit; the minimum frequency modulation coefficient of the system is obtained by combining the system frequency response function, the time of the lowest frequency and the maximum frequency deviation with linear simplification; the limit deep modulation ratio boundary is obtained based on the minimum frequency modulation coefficient of the system and the system frequency modulation coefficient.

[0009] Preferably, obtaining the percentage of deep-tuning unit capacity includes: The primary frequency-regulating synchronous generator units whose output is lower than the preset ratio of rated power in the identification system are denoted as deep-regulating units; the capacity ratio of deep-regulating units is obtained by the ratio of the total rated capacity of all deep-regulating units to the total rated capacity of all primary frequency-regulating synchronous generator units in the system.

[0010] Preferably, the actual frequency regulation coefficient of each deeply regulated unit is calculated based on the frequency regulation mechanism of thermal power units; the system frequency regulation coefficient is obtained based on the capacity ratio of deeply regulated units, the actual frequency regulation coefficient, and the average frequency regulation coefficient of non-deeply regulated units, including: Based on the frequency regulation mechanism of thermal power units and the heat storage characteristic parameters of deep-regulation units, the actual frequency regulation coefficient of deep-regulation units is defined as: ; in, This represents the actual frequency regulation coefficient of the deep-tuning unit; Indicates the frequency regulation parameters of the synchronous generator unit; Indicates the boiler's thermal storage characteristics parameters; Indicates the initial main steam pressure; This represents the droop coefficient for primary frequency regulation of a synchronous generator unit; Indicates the initial valve opening of the synchronous generator unit; Define the average frequency regulation coefficient of non-deep-tuning units as Then the system frequency modulation coefficient Represented as: ; in, This indicates the percentage of capacity of units with deep adjustment capabilities.

[0011] Preferably, the system frequency response function obtained from the system frequency modulation coefficient includes: According to the system frequency modulation coefficient Constructing the system frequency response function System frequency response function The step active power disturbance encountered by the system With the input being the system frequency deviation and the output being the system frequency offset, it can be expressed as: ; ; in, Indicates the system's inherent oscillation frequency; Indicates the damping ratio; The system load damping coefficient is represented by s; s represents the Laplace operator. This represents the equivalent time constant of the steam turbine; This represents the minimum inertia constant of the system under its current state. This represents the proportional coefficient of the high-pressure cylinder's output power.

[0012] Preferably, the time of the lowest frequency and the maximum frequency deviation are obtained from the system frequency response function, including: With regard to the system frequency response function Performing the inverse Laplace transform yields the first expression. : ; Where e is the natural base; t represents time; , , and All are intermediate quantities, represented as: ; Differentiating the first expression and setting the derivative to 0, we obtain the time of the lowest frequency. : ; The moment of lowest frequency Substituting into the first expression, we obtain the maximum frequency deviation in the time domain. : .

[0013] Preferably, if the maximum frequency deviation exceeds the preset range, the risk of frequency exceeding the limit is determined to include: Maximum frequency deviation With power grid safety threshold In comparison, if If so, it is determined that there is a risk of exceeding the frequency limit.

[0014] Preferably, the minimum frequency modulation coefficient of the system is obtained by combining the system frequency response function, the time of the lowest frequency point, and the maximum frequency deviation with linear simplification, including: With regard to the system frequency response function To simplify linearly, the system frequency response model is defined as follows: ; in, This represents the system frequency calculated from the system frequency response model; Indicates the minimum frequency modulation coefficient of the system; Based on the system frequency response model, we obtain Time-domain expression: ; Based on the time of lowest frequency and the maximum frequency deviation, ensure that the frequency of the system frequency response model remains consistent with the actual system frequency at the time of lowest frequency, i.e., reach the maximum frequency deviation: make , , ,but The time-domain expression is simplified to the second expression: ; Solving the second expression, we get The value of is then used to obtain the system's minimum frequency modulation coefficient. .

[0015] Preferably, the boundary of the extreme deep tuning ratio obtained based on the system minimum tuning coefficient and the system tuning coefficient includes: Based on the system minimum frequency modulation coefficient and system frequency modulation coefficient By inversely deriving the expression, the limit depth tuning ratio boundary is obtained. , is represented as: .

[0016] Preferred options also include: Based on the system minimum frequency modulation coefficient With system frequency modulation coefficient The relationship between the two, the limit of the depth of adjustment ratio boundary The proportion of deep-adjustment unit capacity The relationship between them is used to jointly assess the risk of frequency exceeding limits and issue early warnings for frequency security.

[0017] Preferably, frequency over-limit risk assessment and frequency security early warning include: If the proportion of unit capacity is deeply adjusted Greater than or equal to the limit depth adjustment scale boundary And the system frequency modulation coefficient Less than or equal to the system minimum frequency modulation coefficient If so, the current operating state is determined to meet the frequency security requirements; If the proportion of unit capacity is deeply adjusted With the limit depth of adjustment scale boundary The difference reaches a preset threshold, or the system frequency modulation coefficient With the system minimum frequency modulation coefficient If the difference reaches the preset threshold, a frequency safety warning will be output, prompting the dispatcher to reserve one frequency adjustment in advance; If the proportion of unit capacity is deeply adjusted Less than the limit depth adjustment scale boundary or system frequency modulation coefficient Greater than the system minimum frequency modulation coefficient If the current operating status indicates a risk of frequency exceeding the limit, the dispatcher will be guided to adjust the system's frequency regulation capability to restore it to above the safety boundary based on the frequency regulation capability deficit and the deep regulation ratio exceeding the limit.

[0018] The present invention has the following beneficial effects: This invention presents a method for assessing the primary frequency regulation demand of a renewable energy power system under deep peak-shaving conditions. The method defines a coefficient quantifying the capacity attenuation of a single deep peak-shaving unit and uses the deep peak-shaving ratio as a core operating state variable. It establishes an assessment model for the overall frequency regulation capability of aggregated heterogeneous units, achieving high-precision and rapid analytical calculation of frequency drop extremes and their duration. Using grid frequency exceedance standards as constraints, this invention proposes a reverse solution method for the extreme deep peak-shaving boundary oriented towards frequency security, obtaining the extreme deep peak-shaving ratio under system frequency security constraints, providing a basis for subsequent system assessment and early warning. This method not only accurately warns of frequency security risks caused by frequency regulation depletion during periods of high wind and solar power generation but also guides units to further deepen peak regulation during periods of sufficient safety margin, effectively improving the maximum absorption capacity of renewable energy and providing a clear and operable quantitative decision-making tool for the operation of new power systems.

[0019] In addition to the objectives, features, and advantages described above, the present invention has other objectives, features, and advantages. The invention will now be described in further detail with reference to the accompanying drawings. Attached Figure Description

[0020] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a schematic diagram of the method flow of a preferred embodiment of the present invention.

[0021] Figure 2 This is a schematic diagram of the IEEE-39 node system according to a preferred embodiment of the present invention.

[0022] Figure 3 This is a schematic diagram illustrating the system frequency drop under the influence of deep tuning conditions in a preferred embodiment of the present invention.

[0023] Figure 4 This is a schematic diagram illustrating the system's equivalent skew coefficient requirements under the influence of deep-tuning conditions in a preferred embodiment of the present invention.

[0024] Figure 5 This is a schematic diagram of the system state according to a preferred embodiment of the present invention. Detailed Implementation

[0025] The embodiments of the present invention will be described in detail below with reference to the accompanying drawings, but the present invention can be implemented in many different ways as defined and covered by the claims.

[0026] See Figure 1 In a preferred embodiment of the present invention, a method for assessing the primary frequency regulation demand of a new energy power system under deep peak shaving conditions is provided, comprising: S1. Obtain the capacity ratio of deep-regulation units; calculate the actual frequency regulation coefficient of each deep-regulation unit based on the frequency regulation mechanism of thermal power units; obtain the system frequency regulation coefficient based on the capacity ratio of deep-regulation units, the actual frequency regulation coefficient, and the average frequency regulation coefficient of non-deep-regulation units.

[0027] In a preferred embodiment of the present invention, obtaining the capacity ratio of deep-tuning units includes: The primary frequency-regulating synchronous generator units in the identification system whose output is lower than a preset proportion of their rated power are designated as "deep-regulating units." The capacity proportion of deep-regulating units is obtained by comparing the total rated capacity of all deep-regulating units with the total rated capacity of all primary frequency-regulating synchronous generator units in the system. ; in, This indicates the percentage of deep-tuning unit capacity; n represents the number of deep-tuning units. This indicates the rated capacity of i deep-tuning units; This represents the total rated capacity of all primary frequency-regulating synchronous generator units in the system.

[0028] In a preferred embodiment of the present invention, units with output less than 40% of rated power are identified as deep peak-shaving units, because their frequency regulation capability is limited by the rapid decrease in the boiler heat storage coefficient, which will lead to a rapid reduction in primary frequency regulation capability.

[0029] In a preferred embodiment of the present invention, the actual frequency regulation coefficient of each deeply regulated unit is calculated based on the frequency regulation mechanism of thermal power units; the system frequency regulation coefficient is obtained based on the capacity ratio of deeply regulated units, the actual frequency regulation coefficient, and the average frequency regulation coefficient of non-deeply regulated units, including: Based on the frequency regulation mechanism of thermal power units and the heat storage characteristic parameters of deep-regulation units, the actual frequency regulation coefficient of deep-regulation units is defined as: ; in, This represents the actual frequency regulation coefficient of the deep-tuning unit; Indicates the frequency regulation parameters of the synchronous generator unit; Indicates the boiler's thermal storage characteristics parameters; Indicates the initial main steam pressure; This represents the droop coefficient for primary frequency regulation of a synchronous generator unit; Indicates the initial valve opening of the synchronous generator unit; The actual frequency regulation coefficient of a deep-peak unit reflects the reduced frequency regulation capability caused by the dynamic changes in boiler heat storage. The physical mechanism is as follows: After a thermal power unit reduces its output to enter deep peak-shaving operation, the boiler combustion intensity weakens, leading to a decrease in main steam pressure and temperature, and a reduction in the opening of the turbine regulating valves. This deterioration in thermodynamic parameters directly reduces the unit's ability to quickly release boiler heat storage and increase output by opening more regulating valves when frequency disturbances occur.

[0030] Define the average frequency regulation coefficient of non-deep-tuning units as Then the system frequency modulation coefficient Represented as: ; in, This indicates the percentage of capacity of units with deep adjustment capabilities.

[0031] S2. Obtain the system frequency response function based on the system frequency modulation coefficient; obtain the time of the lowest frequency and the maximum frequency deviation based on the system frequency response function.

[0032] In a preferred embodiment of the present invention, obtaining the system frequency response function based on the system frequency modulation coefficient includes: According to the system frequency modulation coefficient Constructing the system frequency response function System frequency response function The step active power disturbance encountered by the system With the input being the system frequency deviation and the output being the system frequency offset, it can be represented in the complex frequency domain as: ; ; in, Indicates the system's inherent oscillation frequency; Indicates the damping ratio; The system load damping coefficient is represented by s; s represents the Laplace operator. This represents the equivalent time constant of the steam turbine; This represents the minimum inertia constant of the system under its current state. This represents the proportional coefficient of the high-pressure cylinder's output power.

[0033] In a preferred embodiment of the present invention, obtaining the time of the lowest frequency and the maximum frequency deviation based on the system frequency response function includes: With regard to the system frequency response function Performing the inverse Laplace transform yields the first expression. : ; Where e is the natural base; t represents time; , , and All are intermediate quantities, represented as: ; Differentiating the first expression and setting the derivative to 0, we obtain the time of the lowest frequency. : ; The moment of lowest frequency Substituting into the first expression, we obtain the maximum frequency deviation in the time domain. : .

[0034] S3. If the maximum frequency deviation exceeds the preset range, it is determined that there is a risk of frequency exceeding the limit. The minimum frequency regulation coefficient of the system is obtained by combining the system frequency response function, the time of the lowest frequency point and the maximum frequency deviation with linear simplification.

[0035] In a preferred embodiment of the present invention, if the maximum frequency deviation exceeds a preset range, determining that there is a risk of frequency exceeding the limit includes: Maximum frequency deviation With power grid safety threshold In comparison, if If so, it is determined that there is a risk of exceeding the frequency limit.

[0036] In a preferred embodiment of the present invention, the minimum frequency modulation coefficient of the system is obtained by combining the system frequency response function, the time of the lowest frequency point, and the maximum frequency deviation with linear simplification, including: because There exists a strong nonlinear mapping involving exponential and trigonometric functions between the frequency modulation coefficient and the frequency response coefficient, making it extremely difficult to directly solve for the required frequency modulation. Therefore, the system frequency response function... To simplify linearly, the system frequency response model is defined as follows: ; in, This represents the system frequency calculated from the system frequency response model; Indicates the minimum frequency modulation coefficient of the system; Based on the system frequency response model, we obtain Time-domain expression: ; Based on the time of lowest frequency and the maximum frequency deviation, ensure that the frequency of the system frequency response model remains consistent with the actual system frequency at the time of lowest frequency, i.e., reach the maximum frequency deviation: make , , ,but The time-domain expression is simplified to the second expression: ; The second expression is obtained by solving mathematical methods (such as Newton's method). The value of is then used to obtain the system's minimum frequency modulation coefficient. .

[0037] S4. Obtain the limit depth modulation ratio boundary based on the system minimum modulation factor and the system modulation factor. S4 specifically includes: Based on the system minimum frequency modulation coefficient and system frequency modulation coefficient By inversely deriving the expression, the limit depth tuning ratio boundary is obtained. , is represented as: .

[0038] In a preferred embodiment of the present invention, it further includes: Based on the system minimum frequency modulation coefficient With system frequency modulation coefficient The relationship between the two, the limit of the depth of adjustment ratio boundary The proportion of deep-adjustment unit capacity The relationship between them is used to jointly assess the risk of frequency exceeding limits and issue early warnings for frequency security.

[0039] In a preferred embodiment of the present invention, frequency over-limit risk assessment and frequency security early warning include: If the proportion of unit capacity is deeply adjusted Greater than or equal to the limit depth adjustment scale boundary And the system frequency modulation coefficient Less than or equal to the system minimum frequency modulation coefficient If so, the current operating state is determined to meet the frequency security requirements; If the proportion of unit capacity is deeply adjusted With the limit depth of adjustment scale boundary The difference reaches a preset threshold, or the system frequency modulation coefficient With the system minimum frequency modulation coefficient If the difference reaches the preset threshold, a frequency safety warning will be output, prompting the dispatcher to reserve one frequency adjustment in advance; If the proportion of unit capacity is deeply adjusted Less than the limit depth adjustment scale boundary or system frequency modulation coefficient Greater than the system minimum frequency modulation coefficient If the current operating status indicates a risk of frequency exceeding the limit, the dispatcher will be guided to adjust the system's frequency regulation capacity to above the safety boundary based on the frequency regulation capacity deficit and the excess of the deep regulation ratio. For example, the dispatcher can be guided to take measures such as increasing the output of deep regulation units, reducing deep peak regulation capacity, activating standby units, calling on energy storage or hydropower rapid frequency regulation resources, and adjusting the output plan of new energy sources.

[0040] This invention presents a method for assessing the primary frequency regulation demand of a renewable energy power system under deep peak-shaving conditions. The method defines a coefficient quantifying the capacity attenuation of a single deep peak-shaving unit and uses the deep peak-shaving ratio as a core operating state variable. It establishes an assessment model for the overall frequency regulation capability of aggregated heterogeneous units, achieving high-precision and rapid analytical calculation of frequency drop extremes and their duration. Using grid frequency exceedance standards as constraints, this invention proposes a reverse solution method for the extreme deep peak-shaving boundary oriented towards frequency security, obtaining the extreme deep peak-shaving ratio under system frequency security constraints, providing a basis for subsequent system assessment and early warning. This method not only accurately warns of frequency security risks caused by frequency regulation depletion during periods of high wind and solar power generation but also guides units to further deepen peak regulation during periods of sufficient safety margin, effectively improving the maximum absorption capacity of renewable energy and providing a clear and operable quantitative decision-making tool for the operation of new power systems.

[0041] Verification section: To verify the effectiveness of the method of this invention, a test system was constructed based on the IEEE-39 node system, such as... Figure 2 As shown.

[0042] To reveal the impact of deep peak shaving on system frequency security risks, a 24-hour hourly time-series simulation evaluation was conducted on the test system. In each time period, a anticipated high-power deficit disturbance was introduced, and two evaluation methods were employed: 1) Traditional method: Ignore deep frequency regulation characteristics and assume that all units have rated frequency regulation capability; 2) Method of the present invention: Taking into account the actual deep adjustment ratio, the method of the present invention is used for evaluation.

[0043] Core assessment results such as Figure 3 As shown in Table 1.

[0044] Table 1 Frequency Drop Values ​​and Deep Tuning Ratio ; Depend on Figure 3 It is evident that if the evaluation method does not consider the impact of deep peak shaving conditions on the unit's frequency regulation capability, the extreme frequency drop values ​​of the system in each time period of 24 hours are all higher than 49.6Hz, seemingly remaining above the system's frequency safety threshold throughout. However, if the method of this invention is used, the results show that in the 11 time periods of 1 to 6 hours, 20 hours, and 22 to 24 hours, the extreme frequency drop values ​​of the system after a large disturbance will exceed the frequency safety threshold. For example, in time period 2, under the anticipated disturbance, the extreme frequency drop value considering the deep peak shaving effect is 49.391Hz, far lower than the evaluation result without considering the deep peak shaving effect (49.642Hz); in time period 6, under the influence of deep peak shaving, the extreme frequency drop value is 49.847Hz, also significantly lower than the traditional calculation result (49.646Hz).

[0045] The reason for the severe frequency drop during the aforementioned period is that during periods of low load (such as 1 to 6 hours), the system's deep peak shaving ratio is reduced to accommodate renewable energy. The frequency regulation capability provided by the deep-modulation units is significantly reduced, leading to an overall decrease in the system's equivalent frequency regulation capability and inevitably resulting in deeper frequency drop extremes. This also reveals that traditional assessment methods that ignore deep peak-shaving conditions systematically overestimate the frequency safety margin. Figure 4 As shown, during the aforementioned high-risk periods, the system's equivalent droop coefficient was consistently lower than the required system droop coefficient. For instance, in the second hour, the actual deep-shaving unit ratio was 0.7159, while the minimum deep-shaving ratio required to meet safety standards was only 0.482. This indicates that by introducing a deep peak-shaving ratio indicator and combining it with the equivalent droop coefficient assessment, the potential weakness in the system's primary frequency regulation capability can be effectively revealed.

[0046] Furthermore, the extreme depth adjustment ratio of the present invention It depicts the critical boundary where the system frequency drops below the safe threshold. For example... Figure 5 As shown, in the 11 time periods of 1 to 6 hours, 20 hours, and 22 to 24 hours, the actual proportion of deep peak-shaving units in the system exceeded the limit for meeting frequency safety constraints. For example, in the 2nd hour, the proportion of deep peak-shaving units was 0.7159, while the limit for deep peak-shaving units was 0.487; in the 20th hour, the proportion of deep peak-shaving units was 0.509, while the limit for deep peak-shaving units was 0.488. Due to the excessive scale of actual deep peak-shaving, the actual frequency drop extreme value of the system in the 20th hour was 49.498Hz, exceeding the frequency safety threshold.

[0047] As the above analysis shows, within a 24-hour operating cycle, the system's frequency security risk is not solely due to excessive disturbances, but is also closely related to the scarcity of effective frequency modulation resources caused by an excessively high deep modulation ratio and insufficient primary frequency modulation capability of the system. Therefore, this invention demonstrates that the method can provide direct and quantitative early warning indicators for preventing such risks in scheduling operations.

[0048] By calculating the system's maximum deep tuning ratio, and without changing the system's operating state, optimized coordination between frequency security and new energy consumption can be achieved by adjusting the synchronous machine power and new energy output. Specifically, for the actual deep tuning ratio... Less than During periods of high risk, the dispatching system must take intervention measures: by increasing the output of some synchronous generators to make them exit deep peak shaving, thereby reducing the actual system load. Reduce to Within this range, the measure comes at the cost of reducing some renewable energy output, but it ensures system frequency security. Regarding the actual frequency regulation coefficient... Greater than During periods of ample safety margin, the output power of the synchronous generator can be further reduced within the safety range, allowing more units to enter deep adjustment mode, thereby maximizing the absorption of new energy power.

[0049] In summary, the method of this invention quantifies the impact of deep peak shaving on the primary frequency regulation of the system and provides a dynamic index for the maximum deep peak shaving ratio that meets frequency security requirements. Using this index, dispatchers can promptly adjust the output of synchronous machines during high-risk periods to ensure grid frequency security, and also tap the potential of deep peak shaving during periods of ample capacity to improve the absorption of new energy sources, thus achieving a balance between safety and economy.

[0050] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for assessing the primary frequency regulation demand of a new energy power system under deep peak-shaving conditions, characterized in that, include: Obtain the capacity ratio of deep-regulation units; calculate the actual frequency regulation coefficient of each deep-regulation unit based on the frequency regulation mechanism of thermal power units; The system frequency regulation coefficient is obtained based on the capacity ratio of the deep-tuning units and the actual frequency regulation coefficient and the average frequency regulation coefficient of the non-deep-tuning units. The system frequency response function is obtained based on the system frequency modulation coefficient; the time of the lowest frequency point and the maximum frequency deviation are obtained based on the system frequency response function; if the maximum frequency deviation exceeds the preset range, it is determined that there is a risk of frequency exceeding the limit; the minimum frequency modulation coefficient of the system is obtained by combining the system frequency response function, the time of the lowest frequency point and the maximum frequency deviation with linear simplification; the limit deep modulation ratio boundary is obtained based on the minimum frequency modulation coefficient of the system and the system frequency modulation coefficient.

2. The method for assessing the primary frequency regulation demand of a new energy power system under deep peak-shaving conditions according to claim 1, characterized in that, The percentage of deep-tuning unit capacity obtained includes: The primary frequency-modulated synchronous generator units whose output is lower than a preset proportion of the rated power in the identification system are denoted as deep-modulated generator units; the capacity proportion of the deep-modulated generator units is obtained by the ratio of the total rated capacity of all deep-modulated generator units to the total rated capacity of all primary frequency-modulated synchronous generator units in the system.

3. The method for assessing the primary frequency regulation demand of a new energy power system under deep peak-shaving conditions according to claim 2, characterized in that, The actual frequency regulation coefficient of each deeply frequency-regulating unit is calculated based on the frequency regulation mechanism of thermal power units; the system frequency regulation coefficient is obtained based on the capacity ratio of the deeply frequency-regulating units, the actual frequency regulation coefficient, and the average frequency regulation coefficient of non-deeply frequency-regulating units, including: Based on the frequency regulation mechanism of thermal power units and the heat storage characteristic parameters of deep-regulation units, the actual frequency regulation coefficient of deep-regulation units is defined as: ； in, This represents the actual frequency regulation coefficient of the deep-tuning unit; Indicates the frequency regulation parameters of the synchronous generator unit; Indicates the boiler's thermal storage characteristics parameters; Indicates the initial main steam pressure; This represents the droop coefficient for primary frequency regulation of a synchronous generator unit; Indicates the initial valve opening of the synchronous generator unit; The average frequency regulation coefficient of the non-deep-tuning unit is defined as follows: Then the system frequency modulation coefficient Represented as: ； in, This indicates the percentage of capacity of units with deep adjustment capabilities.

4. The method for assessing the primary frequency regulation demand of a new energy power system under deep peak-shaving conditions according to claim 3, characterized in that, The system frequency response function obtained based on the system frequency modulation coefficients includes: According to the system frequency modulation coefficient Constructing the system frequency response function The system frequency response function The step active power disturbance encountered by the system With the input being the system frequency deviation and the output being the system frequency offset, it can be expressed as: ； ； in, Indicates the system's inherent oscillation frequency; Indicates the damping ratio; The system load damping coefficient is represented by s; s represents the Laplace operator. This represents the equivalent time constant of the steam turbine; This represents the minimum inertia constant of the system under its current state. This represents the proportional coefficient of the high-pressure cylinder output power.

5. The method for assessing the primary frequency regulation demand of a new energy power system under deep peak-shaving conditions according to claim 4, characterized in that, The time of the lowest frequency and the maximum frequency deviation are obtained from the system frequency response function, including: The system frequency response function Performing the inverse Laplace transform yields the first expression. : ； Where e is the natural base; t represents time; , , and All are intermediate quantities, represented as: ； Taking the derivative of the first expression and setting it to 0, we obtain the time of the lowest frequency point. : ； The time of the lowest frequency Substituting into the first expression, the maximum frequency deviation in the time domain is obtained. : 。 6. The method for assessing the primary frequency regulation demand of a new energy power system under deep peak-shaving conditions according to claim 5, characterized in that, If the maximum frequency deviation exceeds the preset range, the risk of frequency exceeding the limit is determined to include: The maximum frequency deviation With power grid safety threshold In comparison, if If so, it is determined that there is a risk of exceeding the frequency limit.

7. The method for assessing the primary frequency regulation demand of a new energy power system under deep peak-shaving conditions according to claim 6, characterized in that, Based on the system frequency response function, the time of the lowest frequency point, and the maximum frequency deviation, the minimum frequency modulation coefficient of the system is obtained by linear simplification, including: The system frequency response function To simplify linearly, the system frequency response model is defined as follows: ； in, This represents the system frequency calculated from the system frequency response model; Indicates the minimum frequency modulation coefficient of the system; Based on the system frequency response model, we obtain... Time-domain expression: ； Based on the time of the lowest frequency and the maximum frequency deviation, ensure that the frequency of the system frequency response model remains consistent with the actual system frequency at the time of the lowest frequency, i.e., achieve the maximum frequency deviation: make , , ,but The time-domain expression is simplified to the second expression: ； Solving the second expression yields the value of x, and thus the minimum frequency modulation coefficient of the system. .

8. The method for assessing the primary frequency regulation demand of a new energy power system under deep peak-shaving conditions according to claim 7, characterized in that, The limit depth modulation ratio boundary is obtained based on the system minimum modulation coefficient and the system modulation coefficient, including: According to the system minimum frequency modulation coefficient and the system frequency modulation coefficient By inversely deriving the expression, the limit depth tuning ratio boundary is obtained. , is represented as: 。 9. The method for assessing the primary frequency regulation demand of a new energy power system under deep peak-shaving conditions according to claim 8, characterized in that, Also includes: According to the system minimum frequency modulation coefficient With the system frequency modulation coefficient The relationship between the two, and the limit depth adjustment ratio boundary The percentage of the capacity of the deep-tuning unit The relationship between them is used to jointly assess the risk of frequency exceeding limits and issue early warnings for frequency security.

10. The method for assessing the primary frequency regulation demand of a new energy power system under deep peak-shaving conditions according to claim 9, characterized in that, Frequency over-limit risk assessment and frequency security warning include: If the proportion of unit capacity is deeply adjusted Greater than or equal to the limit depth adjustment ratio boundary And the system frequency modulation coefficient Less than or equal to the system's minimum frequency modulation coefficient If so, the current operating state is determined to meet the frequency security requirements; If the proportion of unit capacity is deeply adjusted With the aforementioned limit depth adjustment ratio boundary The difference reaches a preset threshold, or the system frequency modulation coefficient With the minimum frequency modulation coefficient of the system If the difference reaches the preset threshold, a frequency safety warning will be output, prompting the dispatcher to reserve one frequency adjustment in advance; If the proportion of unit capacity is deeply adjusted Smaller than the limit depth adjustment ratio boundary or the system frequency modulation coefficient Greater than the minimum frequency modulation coefficient of the system If the current operating status indicates a risk of frequency exceeding the limit, the dispatcher will be guided to adjust the system's frequency regulation capability to restore it to above the safety boundary based on the frequency regulation capability deficit and the deep regulation ratio exceeding the limit.

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