Dynamic quantitative evaluation method for primary and secondary frequency modulation capabilities of power grid

Abstract

The invention belongs to the technical field of power grid frequency modulation, and particularly relates to a power grid primary and secondary frequency modulation capability dynamic quantitative evaluation method. The method comprises the following steps: establishing a prediction model by utilizing primary and secondary frequency modulation capability evaluation indexes of various regulation resources; performing participation degree prediction on various frequency modulation resources by using the data and the prediction model; the method comprises the following steps: predicting primary and secondary frequency modulation capabilities in wind power, photovoltaic and hydroelectric power plants when frequency fluctuation occurs in a power grid, allocating primary and secondary frequency modulation tasks, and determining participation degrees of various regulation resource power plants when the frequency fluctuation occurs in the power grid; a power grid frequency response model integrating a plurality of adjustment resources and considering space-time and meteorological factors; and performing dynamic quantitative evaluation on the primary and secondary frequency modulation capabilities of the system by using the primary and secondary frequency modulation capability evaluation indexes of the whole system. Decision support can be provided for a power grid dispatching center, the frequency modulation strategy is optimized, the stability and safety of a power grid are improved, and the increasingly complex power system frequency problem in the future is solved.

Description

Technical Field

[0001] This invention belongs to the field of power grid frequency regulation technology, and in particular relates to a dynamic quantitative evaluation method for the primary and secondary frequency regulation capabilities of a power grid. Background Technology

[0002] Currently, my country's wind and solar power generation are experiencing a new leapfrog development. It is predicted that by 2030, the installed capacity of new energy sources will increase by 1.07-1.27 TW, reaching a total of 1.6-1.8 TW. However, the inherent volatility, randomness, and the electronic nature of grid-connected equipment in new energy power generation have significantly impacted the future power quality and operation scheduling of the power grid, limiting the overall absorption capacity of new energy by the grid to some extent. Simultaneously, the two new characteristics of the power grid—a high proportion of renewable energy and a high proportion of power electronic equipment—have led to a significant decrease in system inertia and spinning reserve capacity. On September 19, 2015, the Jin-Su ±800kV UHVDC transmission project, the world's largest and longest-distance transmission line, experienced a bipolar blocking event, resulting in a significant power shortage that caused the frequency of the East China power grid to drop to a historical low of 49.56Hz. On August 9, 2019, the major power outage in the UK was the largest blackout in recent years caused by new energy sources, resulting in incalculable economic losses and adverse social impacts. The power outage was caused by the large-scale development of new energy sources, which led to excessively low system inertia. When a grid fault occurred, the frequency dropped rapidly, triggering the low-frequency load shedding protection.

[0003] Therefore, modeling and analysis methods for predicting power grid frequency response are particularly important. Traditional power grid frequency response models often only consider synchronous machines and ignore scenarios where regulating resources such as wind, solar, hydro, and energy storage are integrated into the grid. With the continuous increase in the demand for power grid frequency regulation, more and more new energy sources will be accompanied by new control strategies, such as virtual synchronous machine control, to actively participate in the primary frequency regulation and inertial response of the power grid. Establishing a dynamic quantitative evaluation model for the primary and secondary frequency regulation capabilities of the power system is of great significance for ensuring the safe operation of the power system frequency. Summary of the Invention

[0004] To address the shortcomings of the existing technologies, this invention provides a dynamic quantitative assessment method for the primary and secondary frequency regulation capabilities of a power grid. Its purpose is to enable more accurate prediction of frequency regulation capability fluctuations of various regulatory resources, thereby facilitating the allocation of frequency regulation tasks and providing convenience for power grid dispatch centers.

[0005] The technical solution adopted by the present invention to achieve the above objectives is as follows:

[0006] A method for dynamic quantitative evaluation of the primary and secondary frequency regulation capabilities of a power grid, comprising:

[0007] Establish characteristic indicators of primary and secondary frequency regulation capabilities of the power grid based on the characteristics of frequency regulation of thermal power, wind power, photovoltaic power, hydropower, and energy storage.

[0008] Models are established based on the characteristic indicators of the primary and secondary frequency regulation capabilities of the power grid to predict the primary and secondary frequency regulation capabilities of the power grid at their respective time scales.

[0009] Based on the prediction model, the primary and secondary frequency regulation capabilities of various power plants are predicted in real time when the power grid experiences frequency fluctuations, and the participation of various regulating power plants in frequency fluctuations is determined.

[0010] Establish an energy storage-assisted frequency regulation model based on the participation of various regulatory resources and power plants;

[0011] By combining the predictive model with the energy storage-assisted frequency regulation model with the traditional frequency response model, a power grid frequency response model that integrates multiple regulation resources and considers spatiotemporal and meteorological factors is obtained.

[0012] Based on the power grid frequency response model, evaluation indicators for the primary and secondary frequency regulation capabilities of the entire system are established to dynamically and quantitatively assess the primary and secondary frequency regulation capabilities of the system.

[0013] Furthermore, the establishment of characteristic indicators for the primary and secondary frequency regulation capabilities of the power grid based on the characteristics of frequency regulation by thermal power and wind power, photovoltaic power, hydropower, and energy storage includes:

[0014] Response time: The time required for the unit to reach a new steady-state output from receiving an instruction, measured in seconds (s).

[0015] t res =t rec -t s

[0016] In the formula: t res For response time, t rec t is the time when the unit receives the instruction. s The time it takes for the unit to reach steady state;

[0017] Maximum regulation capacity: The maximum range of power increase or decrease that the unit can provide during frequency regulation, in megawatts (MW);

[0018] Minimum regulating capacity: The minimum power output at which the unit can operate stably, measured in megawatts (MW).

[0019] Overshoot: The maximum extent by which the unit's frequency exceeds the target frequency after the unit responds;

[0020] Phase delay: The delay in the unit's response to frequency changes;

[0021] Continuous frequency regulation capability: The length of time that the unit can continuously provide regulating power while maintaining frequency regulation, in seconds (s);

[0022] Dynamic gain: The proportional relationship between changes in unit output power and frequency, reflecting the unit's sensitivity to frequency changes;

[0023] Reserve capacity: The capacity of standby generating units used for frequency regulation, reflecting the system's ability to cope with sudden load changes or failures. Unit: megawatt (MW).

[0024] The above indicators are linearly combined as follows to obtain the evaluation indicators of the primary and secondary frequency regulation capabilities of various regulation resources.

[0025] R=α1t res +α2P cmax +α3P cmin +α4σ+α5K+α6θ+α7t con +α8P b

[0026]

[0027] In the formula: t res For response time, P cmax For maximum regulation capacity, P cmin For minimum adjustment capability, σ is the overshoot, θ is the phase delay, and t con For continuous frequency modulation capability, P b This is for backup capacity.

[0028] Furthermore, the model for predicting the primary and secondary frequency regulation capabilities of the power grid at their respective time scales, based on the characteristic indicators of the primary and secondary frequency regulation capabilities of the power grid, is as follows:

[0029] R=α1t res +α2P cmax +α3P cmin +α4σ+α5K+α6θ+α7t con +α8P b

[0030]

[0031] Where, α i The weight of each factor in the frequency modulation system depends on the characteristics of various regulation resources and the frequency modulation work involved.

[0032] Furthermore, the method involves predicting the primary and secondary frequency regulation capabilities of various power plants in real time when the power grid experiences frequency fluctuations based on a prediction model, and determining the participation degree of various regulating power plants when the power grid experiences frequency fluctuations, wherein:

[0033] The frequency regulation capability R of various regulation resources is calculated using a predictive model. i Assign primary and secondary frequency modulation tasks, according to R i Determine the participation β of various regulating power plants when grid frequency fluctuations occur. i :

[0034]

[0035] Furthermore, the establishment of an energy storage-assisted frequency regulation model based on the participation of various regulating power plants includes:

[0036] Define ΔP as the power deficit of the system after frequency regulation by thermal power, wind power, photovoltaic power, and hydropower:

[0037]

[0038] Where P is the power required for frequency modulation, P i Useful power contributed to thermal power, wind power, photovoltaic power and hydropower; the contribution of hydropower can be positive or negative;

[0039] The energy storage-assisted frequency regulation model is divided into frequency regulation charging mode and frequency regulation discharging mode based on ΔP:

[0040] If ΔP>0, the energy storage auxiliary system is in frequency regulation charging mode;

[0041] If ΔP < 0, the energy storage auxiliary system is in frequency modulation discharge mode.

[0042] Furthermore, by combining the predictive model with the energy storage-assisted frequency regulation model and the traditional frequency response model, a power grid frequency response model that integrates multiple regulation resources and considers spatiotemporal and meteorological factors is obtained, including:

[0043] Each frequency regulation unit model is a combination of the corresponding traditional frequency response model, prediction model, and participation degree to obtain a frequency response model for dynamic quantitative evaluation of the primary and secondary frequency regulation capabilities of the power grid.

[0044] The frequency response model of a traditional thermal power unit is:

[0045]

[0046] Among them, F HP T is the mechanical power proportionality coefficient of the cylinder. CH T RH Let be the volumetric effect time constant of the cylinder, and s be the transfer function variable;

[0047] The traditional frequency response model for wind power units is:

[0048]

[0049] Among them, T ω k is the inertial response time constant. df ΔP is the inertial response coefficient. ω Δf is the change in inertial control power; k is the change in power system frequency. pf T is the primary frequency modulation coefficient. β Variable pitch response time constant;

[0050] The traditional frequency response model for hydropower units is:

[0051]

[0052] Among them, T wa The inertial time constant of the water flow in the hydroelectric generator unit;

[0053] The traditional frequency response model of a photovoltaic frequency modulation unit is:

[0054] G pv (s)=(k pv C b U dczero +H pv )s

[0055] Where, k pv For photovoltaic control parameters, H pv C is the virtual inertia time constant. b For high voltage DC capacitors, U dczero This is the initial value of the high voltage DC voltage;

[0056] The traditional energy storage frequency response model is:

[0057]

[0058] Where, k E Battery gain, T E It is a specific timeframe for how battery energy storage can sustain its function.

[0059] Incorporate a secondary frequency modulation model;

[0060] If a single-zone frequency modulation uses a fixed-frequency control mode, and if two zones are interconnected, the above TBC-TBC mode is used. The ACE calculation formula is as follows:

[0061] ACE = -B × Δf

[0062] ACE = ΔP 12 +B×Δf

[0063] Where B is the frequency ratio deviation coefficient, P is the actual power on all tie lines in the two control zones, and the secondary frequency modulation task at the corresponding time is determined based on the sign of the ACE value.

[0064] Furthermore, based on the power grid frequency response model, evaluation indicators for the primary and secondary frequency regulation capabilities of the entire system are established to dynamically and quantitatively assess the primary and secondary frequency regulation capabilities of the system, including:

[0065] Establish evaluation indicators for the frequency regulation capability of the entire power grid:

[0066]

[0067] Where Δf0 is the rate of change of frequency, Δf max For the maximum frequency deviation, Δf s For steady-state frequency deviation, K L k1, k2, and k3 are proportionality coefficients;

[0068] The primary and secondary frequency regulation capabilities of the system are dynamically and quantitatively evaluated using evaluation indicators and power grid frequency response models.

[0069] The frequency stability of the entire system is examined using the rate of change of frequency, maximum frequency deviation, and steady-state frequency deviation as boundary indices. The frequency stability indices of the system are analyzed, and the frequency modulation-related parameters are theoretically analyzed from the frequency domain perspective, as shown in the following equation:

[0070]

[0071] In the above formula, K L This is the gain coefficient, used for ease of calculation and plotting.

[0072] A dynamic quantitative evaluation device for the primary and secondary frequency regulation capabilities of a power grid, comprising:

[0073] The characteristic index establishment module is used to establish characteristic indicators of the primary and secondary frequency regulation capabilities of the power grid based on the characteristics of frequency regulation of thermal power and frequency regulation of wind power, photovoltaic power, hydropower, and energy storage.

[0074] The prediction module is used to establish models for predicting the primary and secondary frequency regulation capabilities of the power grid at their respective time scales based on the characteristic indicators of the primary and secondary frequency regulation capabilities of the power grid.

[0075] The participation determination module is used to predict the primary and secondary frequency regulation capabilities of various power plants in real time when the power grid experiences frequency fluctuations, based on the prediction model, and to determine the participation of various regulating power plants when the power grid experiences frequency fluctuations.

[0076] The energy storage-assisted frequency regulation model establishment module is used to establish an energy storage-assisted frequency regulation model based on the participation of power plants in various regulatory resources.

[0077] The power grid frequency response model building module is used to combine the prediction model, the energy storage-assisted frequency regulation model, and the traditional frequency response model to obtain a power grid frequency response model that integrates multiple regulation resources and considers spatiotemporal and meteorological factors.

[0078] The evaluation module is used to establish evaluation indicators for the primary and secondary frequency regulation capabilities of the entire system based on the power grid frequency response model, and to dynamically and quantitatively evaluate the primary and secondary frequency regulation capabilities of the system.

[0079] Furthermore, the model for predicting the primary and secondary frequency regulation capabilities of the power grid at their respective time scales, based on the characteristic indicators of the primary and secondary frequency regulation capabilities of the power grid, is as follows:

[0080] R=α1t res +α2P cmax +α3P cmin +α4σ+α5K+α6θ+α7t con +α8P b

[0081]

[0082] Where, α i The weight of each factor in the frequency modulation system depends on the characteristics of various regulation resources and the frequency modulation work involved.

[0083] A computer device includes a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, it implements the steps of the dynamic quantitative evaluation method for primary and secondary frequency regulation capabilities of a power grid as described in any one of the claims.

[0084] A computer storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements the steps of any of the methods for dynamic quantitative evaluation of primary and secondary frequency regulation capabilities of a power grid.

[0085] The present invention has the following beneficial effects and advantages:

[0086] This invention comprehensively considers the dynamic response characteristics of various regulation resources, such as wind power, solar power, thermal power, hydropower, and energy storage, at different time scales, and conducts in-depth research on the interaction and synergistic effects of these resources in grid frequency regulation. It analyzes the characteristic differences of thermal power in the frequency regulation process and its complementarity with wind power, photovoltaic power, hydropower, and energy storage.

[0087] A systematic analysis of the dynamic response characteristics of various regulation resources is conducted, including their response speed, regulation capacity, and impact on power grid frequency fluctuations under different climatic conditions. Mathematical models are established by combining the unique advantages of various regulation resources to quantify their contributions to frequency regulation and evaluate optimal schemes for different resource combinations. A dynamic quantitative evaluation model of the primary and secondary frequency regulation capabilities of the power grid is then established using the optimized scheme. This model will provide decision support for the power grid dispatch center, optimize frequency regulation strategies, and improve the stability and security of the power grid to address the increasingly complex frequency issues of future power systems.

[0088] This invention establishes a real-time frequency response model that integrates various frequency regulation resources, taking into account the different factors affecting different frequency regulation resources and their specific characteristics. Compared with traditional frequency regulation models, this model, by incorporating various frequency regulation resources with different characteristics, fully utilizes the specificity and complementarity of these resources, enabling it to more accurately predict fluctuations in the frequency regulation capabilities of various regulation resources and thus allocate frequency regulation tasks. This invention integrates primary and secondary frequency regulation into the same frequency regulation model, providing convenience for power grid dispatch centers. Attached Figure Description

[0089] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0090] Figure 1 This is a flowchart of the dynamic quantitative evaluation method for the primary and secondary frequency regulation capabilities of the power grid according to the present invention.

[0091] Figure 2 This is a diagram of the characteristic index system for evaluating the primary and secondary frequency regulation capabilities of various regulation resources in this invention;

[0092] Figure 3 This is a diagram of the evaluation index system for the primary and secondary frequency modulation capabilities of the system of the present invention;

[0093] Figure 4 This is a frequency response model diagram for the dynamic quantitative evaluation of the primary and secondary frequency regulation capabilities of the power grid according to the present invention.

[0094] Figure 5 This is a schematic diagram illustrating the use of the dynamic quantitative evaluation model for the primary and secondary frequency regulation capabilities of the power grid in this invention.

[0095] Figure 6 This is a flowchart of the dynamic quantitative evaluation model for the primary and secondary frequency regulation capabilities of the power grid according to the present invention. Detailed Implementation

[0096] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments of the present invention and the features thereof can be combined with each other.

[0097] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0098] The following reference Figures 1-6 The technical solutions of some embodiments of the present invention are described below.

[0099] Example 1

[0100] This invention provides an embodiment of a method for dynamic quantitative evaluation of the primary and secondary frequency regulation capabilities of a power grid. For example... Figure 1 As shown, Figure 1 This is a flowchart of the dynamic quantitative evaluation method for the primary and secondary frequency regulation capabilities of a power grid according to the present invention. The present invention integrates multiple regulation resources to construct a dynamic quantitative evaluation method for the primary and secondary frequency regulation capabilities of a power grid.

[0101] A method for dynamic quantitative evaluation of the primary and secondary frequency regulation capabilities of a power grid includes the following steps:

[0102] Step 1. Establish a characteristic index of primary and secondary frequency regulation capabilities of the power grid based on the characteristics of frequency regulation of thermal power, wind power, photovoltaic power, hydropower, and energy storage.

[0103] Step 2. Based on the characteristic indicators of the primary and secondary frequency regulation capabilities of the power grid established in Step 1, establish a model that can predict the primary and secondary frequency regulation capabilities of the power grid at their respective time scales.

[0104] Step 3. Based on the prediction model established in Step 2, predict in real time the primary and secondary frequency regulation capabilities of thermal power, wind power, photovoltaic power and hydropower plants when the grid frequency fluctuates, allocate primary and secondary frequency regulation tasks, and determine the participation of various regulation resources power plants when the grid frequency fluctuates.

[0105] Step 4. Based on the frequency regulation capabilities of various frequency regulation resources, i.e. the participation of power plants in various regulation resources, establish an energy storage-assisted frequency regulation model.

[0106] Step 5. Combine the prediction model established in Step 2 with the energy storage-assisted frequency regulation model established in Step 4 and the traditional frequency response model to obtain a power grid frequency response model that integrates multiple regulation resources and considers spatiotemporal and meteorological factors.

[0107] Step 6. Based on the power grid frequency response model, establish evaluation indicators for the primary and secondary frequency regulation capabilities of the entire system, and use these indicators to dynamically and quantitatively evaluate the primary and secondary frequency regulation capabilities of the system.

[0108] In step 1, a characteristic index for the primary and secondary frequency regulation capabilities of the power grid is established based on the characteristics of frequency regulation by thermal power, wind power, photovoltaic power, hydropower, and energy storage. For example... Figure 1 As shown, Figure 1 This is a diagram of the characteristic index system for evaluating the primary and secondary frequency regulation capabilities of various regulation resources in this invention.

[0109] The specific evaluation characteristics of the primary and secondary frequency regulation capabilities of various regulation resources in this invention include: response time, maximum regulation capability, minimum regulation capability, overshoot, dynamic gain, phase delay, continuous frequency regulation capability, and reserve capacity.

[0110] The response time is the time required for the unit to reach a new steady-state output from receiving an instruction, measured in seconds (s).

[0111] t res =t rec -t s

[0112] In the formula: t res For response time, t rec t is the time when the unit receives the instruction. s This refers to the time it takes for the unit to reach steady state.

[0113] The maximum adjustment capability P cmax The maximum power increase or decrease range that the unit can provide during frequency regulation, in megawatts (MW).

[0114] The minimum adjustment capability P cmin Minimum power output required for stable operation of the generator unit, measured in megawatts (MW).

[0115] The overshoot σ is the maximum extent by which the unit's frequency exceeds the target frequency after the unit responds.

[0116] The phase delay θ: the delay in the unit's response to frequency changes, usually expressed as a phase angle.

[0117] The continuous frequency modulation capability t con The duration for which a generator can continuously provide regulating power while maintaining frequency regulation, measured in seconds (s).

[0118] Dynamic gain K: The proportional relationship between the change in unit output power and the change in frequency, reflecting the unit's sensitivity to frequency changes.

[0119] The spare capacity P b: The capacity of standby generating units that can be used for frequency regulation reflects the system's ability to cope with sudden load changes or failures. The unit is megawatt (MW).

[0120] The above indicators vary among different regulating resource power plants, reflecting their differences and complementarity.

[0121] By performing the above indicators in a linear combination, we can obtain the evaluation indicators for the primary and secondary frequency regulation capabilities of various regulation resources.

[0122] R=α1t res +α2P cmax +α3P cmin +α4σ+α5K+α6θ+α7t con +α8P b

[0123]

[0124] In the formula: t res For response time, P cmax For maximum regulation capacity, P cmin For minimum adjustment capability, σ is the overshoot, θ is the phase delay, and t con For continuous frequency modulation capability, P b This is for backup capacity.

[0125] Step 2, which involves establishing a model based on the frequency modulation capability characteristic indicators established in Step 1 to predict the primary and secondary frequency modulation capabilities at their respective time scales, specifically includes the following steps:

[0126] Step 2: Based on the characteristic index variables of the primary and secondary frequency regulation capabilities of the power grid, the prediction model is established as follows:

[0127] R=α1t res +α2P cmax +α3P cmin +α4σ+α5K+α6θ+α7t con +α8P b

[0128]

[0129] Where, α i The weight of each factor in the frequency modulation system depends on the characteristics of various regulation resources and the frequency modulation work involved.

[0130] In step 3, the prediction model established in step 2 is used to predict in real time the primary and secondary frequency regulation capabilities of thermal power, wind power, photovoltaic power, and hydropower plants when the power grid experiences frequency fluctuations. Primary and secondary frequency regulation tasks are then allocated, and the participation rate of various regulating power plants in frequency fluctuations is determined. Specifically, the frequency regulation capability R of various regulating resources is calculated using the model established in step 2. i Assign primary and secondary frequency modulation tasks according to R. i Determine the participation β of various regulating power plants when grid frequency fluctuations occur. i :

[0131]

[0132] Step 4, which involves establishing an energy storage-assisted frequency regulation model based on the frequency regulation capabilities of various frequency regulation resources, includes the following specific steps:

[0133] Step 4.1 Define ΔP as the power deficit of the system after frequency regulation by thermal power, wind power, photovoltaic power and hydropower.

[0134]

[0135] Where P is the power required for frequency modulation, P i The useful power contributed to thermal power, wind power, photovoltaic power, and hydropower. Among them, the contribution of hydropower can be positive or negative.

[0136] Step 4.2 The energy storage-assisted frequency regulation model is divided into frequency regulation charging mode and frequency regulation discharging mode according to ΔP.

[0137] If ΔP>0, the energy storage auxiliary system is in frequency regulation charging mode;

[0138] If ΔP < 0, the energy storage auxiliary system is in frequency modulation discharge mode.

[0139] like Figure 4 As shown, Figure 4 This invention relates to a frequency response model for the dynamic quantitative evaluation of the primary and secondary frequency regulation capabilities of a power grid. Specifically, step 5 involves combining the prediction model established in step 2 with the energy storage-assisted frequency regulation model established in step 4, along with the traditional frequency response model, to obtain a power grid frequency response model that integrates multiple regulation resources and considers spatiotemporal and meteorological factors. The specific steps are as follows:

[0140] Step 5.1 The model of each frequency regulation unit is a combination of the corresponding traditional frequency response model, the prediction model in step two, and the participation degree in step three, to obtain the frequency response model for dynamic quantitative evaluation of the primary and secondary frequency regulation capabilities of the power grid.

[0141] The frequency response model of a traditional thermal power unit is:

[0142]

[0143] Among them, F HP T is the mechanical power proportionality coefficient of the cylinder. CH T RH Let be the volumetric effect time constant of the cylinder, and s be the transfer function variable.

[0144] The traditional frequency response model for wind power units is:

[0145]

[0146] Among them, T ω k is the inertial response time constant. df ΔP is the inertial response coefficient. ω Δf is the change in inertial control power; k is the change in power system frequency. pf T is the primary frequency modulation coefficient. β Variable pitch response time constant.

[0147] The traditional frequency response model for hydropower units is:

[0148]

[0149] Among them, T wa is the inertial time constant of the water flow in the hydroelectric generator unit.

[0150] The traditional frequency response model of a photovoltaic frequency modulation unit is:

[0151] G pv (s)=(k pv C b U dczero +H pv )s

[0152] Where, k pv For photovoltaic control parameters, H pv C is the virtual inertia time constant. b For high voltage DC capacitors, U dczero This is the initial value of the high voltage DC voltage.

[0153] The traditional energy storage frequency response model is:

[0154]

[0155] Where, k E Battery gain, T E It is a specific time expression regarding the continuous function of battery energy storage.

[0156] Step 5.2 Add the secondary frequency modulation model.

[0157] If a single-zone frequency modulation uses a fixed-frequency control mode, and if two zones are interconnected, the above TBC-TBC mode is used. The ACE calculation formula is as follows:

[0158] ACE = -B × Δf

[0159] ACE = ΔP 12 +B×Δf

[0160] Where B is the frequency ratio deviation coefficient, and P is the actual sum of power on all tie lines in the two control zones. The secondary frequency modulation task at the corresponding time is determined based on the sign of the ACE value.

[0161] In specific implementation, such as Figure 5 As shown, Figure 5 This is a schematic diagram illustrating the use of the dynamic quantitative evaluation model for the primary and secondary frequency regulation capabilities of the power grid according to the present invention.

[0162] 1. Characteristics of power grid secondary frequency regulation capability: acquire data related to frequency regulation capability; Data center: obtain various data resources required for operation prediction models.

[0163] 2. Predictive Model: Data is fed into the predictive model to predict the future frequency regulation capability of the power grid, so as to formulate response strategies in advance.

[0164] 3. Frequency Response Model: Based on the prediction, a frequency response model is used to simulate the frequency regulation process of the power grid in order to analyze the frequency response of the power grid under different conditions.

[0165] 4. Quantitative evaluation: The frequency regulation capability of the power grid is evaluated using the evaluation indicators of the frequency regulation capability of the entire system.

[0166] like Figure 6 As shown, Figure 6 This is a flowchart of the dynamic quantitative evaluation model for the primary and secondary frequency regulation capabilities of the power grid according to the present invention.

[0167] 1. Load disturbances input into the system generate frequency fluctuations after passing through system inertia and damping.

[0168] 2. Perform a primary frequency regulation using wind, solar, hydro, and thermal frequency regulation units, while also considering whether to charge the energy storage auxiliary frequency regulation module.

[0169] 3. After the first frequency regulation, a second frequency regulation is carried out. Dispatch instructions are issued to the wind, solar, hydro and thermal frequency regulation units, and at the same time, it is considered whether energy storage units are needed to assist in the frequency regulation.

[0170] 4. Complete the entire frequency modulation process.

[0171] Step 6 involves establishing evaluation indicators for the primary and secondary frequency modulation capabilities of the entire system. These indicators are used to dynamically and quantitatively assess the primary and secondary frequency modulation capabilities of the system. Specifically, this includes the following steps:

[0172] Step 6.1 Establish evaluation indicators for the frequency regulation capability of the entire power grid:

[0173]

[0174] Where Δf0 is the rate of change of frequency, Δf max For the maximum frequency deviation, Δf s For steady-state frequency deviation, K L k1, k2, and k3 are proportionality coefficients.

[0175] Step 6.2 Use the evaluation index established in Step 6.1 and the power grid frequency response model established in Step 5 to dynamically and quantitatively evaluate the primary and secondary frequency regulation capabilities of the system.

[0176] like Figure 2 As shown, Figure 2 This is a diagram of the evaluation index system for the primary and secondary frequency modulation capabilities of the entire system of this invention.

[0177] The frequency stability of the entire system is examined using the rate of change of frequency, maximum frequency deviation, and steady-state frequency deviation as boundary indicators. The system's frequency stability indicators are analyzed using the following formula, and the frequency modulation-related parameters are theoretically analyzed from a frequency domain perspective, as shown in the following equation:

[0178]

[0179] In the above formula, K L This is the gain coefficient, used for ease of calculation and plotting.

[0180] Example 2

[0181] The present invention provides another embodiment of a dynamic quantitative evaluation device for the primary and secondary frequency regulation capabilities of a power grid, comprising:

[0182] The characteristic index establishment module is used to establish characteristic indicators of the primary and secondary frequency regulation capabilities of the power grid based on the characteristics of frequency regulation of thermal power and frequency regulation of wind power, photovoltaic power, hydropower, and energy storage.

[0183] The prediction module is used to build a model that can predict the primary and secondary frequency regulation capabilities of the power grid at their respective time scales, based on the established characteristic indicators of the primary and secondary frequency regulation capabilities of the power grid.

[0184] The participation determination module is used to predict in real time the primary and secondary frequency regulation capabilities of thermal power, wind power, photovoltaic power and hydropower plants when the grid frequency fluctuates, based on the established prediction model, allocate primary and secondary frequency regulation tasks, and determine the participation of various regulation resource power plants when the grid frequency fluctuates.

[0185] The energy storage-assisted frequency regulation model establishment module is used to establish an energy storage-assisted frequency regulation model based on the frequency regulation capabilities of various frequency regulation resources, i.e., the participation of power plants in regulating resources.

[0186] The power grid frequency response model establishment module is used to combine the established prediction model with the energy storage-assisted frequency regulation model and the traditional frequency response model to obtain a power grid frequency response model that integrates multiple regulation resources and considers spatiotemporal and meteorological factors.

[0187] The evaluation module is used to establish evaluation indicators for the primary and secondary frequency regulation capabilities of the entire system based on the power grid frequency response model, and to use these indicators to dynamically and quantitatively evaluate the primary and secondary frequency regulation capabilities of the system.

[0188] The model established based on the characteristic indicators of the primary and secondary frequency regulation capabilities of the power grid, capable of predicting the primary and secondary frequency regulation capabilities of the power grid at their respective time scales, is as follows:

[0189] R=α1t res +α2P cmax +α3P cmin +α4σ+α5K+α6θ+α7t con +α8P b

[0190]

[0191] Where, α i The weight of each factor in the frequency modulation system depends on the characteristics of various regulation resources and the frequency modulation work involved.

[0192] The dynamic quantitative evaluation device for primary and secondary frequency regulation capabilities of a power grid described in this embodiment is used to implement the steps of the dynamic quantitative evaluation method for primary and secondary frequency regulation capabilities of a power grid described in Embodiment 1.

[0193] Example 3

[0194] Based on the same inventive concept, embodiments of the present invention also provide a computer device, including a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor. When the processor executes the computer program, it implements the steps of any of the methods for dynamic quantitative evaluation of primary and secondary frequency regulation capabilities of a power grid as described in Embodiment 1.

[0195] Example 4

[0196] Based on the same inventive concept, this embodiment of the invention also provides a computer storage medium storing a computer program, which, when executed by a processor, implements the steps of any one of the dynamic quantitative evaluation methods for primary and secondary frequency regulation capabilities of a power grid as described in Embodiment 1.

[0197] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0198] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0199] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0200] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0201] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A dynamic quantitative evaluation method for the primary and secondary frequency regulation capabilities of a power grid, characterized by: include: Establish characteristic indicators of primary and secondary frequency regulation capabilities of the power grid based on the characteristics of frequency regulation of thermal power, wind power, photovoltaic power, hydropower, and energy storage. Models are established based on the characteristic indicators of the primary and secondary frequency regulation capabilities of the power grid to predict the primary and secondary frequency regulation capabilities of the power grid at their respective time scales. Based on the prediction model, the primary and secondary frequency regulation capabilities of various power plants are predicted in real time when the power grid experiences frequency fluctuations, and the participation of various regulating power plants in frequency fluctuations is determined. Establish an energy storage-assisted frequency regulation model based on the participation of various regulatory resources and power plants; By combining the predictive model with the energy storage-assisted frequency regulation model with the traditional frequency response model, a power grid frequency response model that integrates multiple regulation resources and considers spatiotemporal and meteorological factors is obtained. Based on the power grid frequency response model, evaluation indicators for the primary and secondary frequency regulation capabilities of the entire system are established to dynamically and quantitatively assess the primary and secondary frequency regulation capabilities of the system.

2. The method for dynamic quantitative evaluation of primary and secondary frequency regulation capabilities of a power grid according to claim 1, characterized in that: The establishment of primary and secondary frequency regulation capability characteristic indicators for the power grid based on the characteristics of frequency regulation by thermal power and wind power, photovoltaic power, hydropower, and energy storage includes: Response time: The time required for the unit to reach a new steady-state output from receiving an instruction, measured in seconds (s). t res =t rec -t s In the formula: t res For response time, t rec t is the time when the unit receives the instruction. s The time it takes for the unit to reach steady state; Maximum regulation capacity: The maximum range of power increase or decrease that the unit can provide during frequency regulation, in megawatts (MW); Minimum regulation capacity: The minimum power output that the unit can stably operate in, in megawatts (MW); Overshoot: The maximum extent by which the unit's frequency exceeds the target frequency after the unit responds; Phase delay: The delay in the unit's response to frequency changes; Continuous frequency regulation capability: The length of time that the unit can continuously provide regulating power while maintaining frequency regulation, in seconds (s); Dynamic gain: The proportional relationship between changes in unit output power and frequency, reflecting the unit's sensitivity to frequency changes; Reserve capacity: The capacity of standby generating units used for frequency regulation, reflecting the system's ability to cope with sudden load changes or failures. Unit: megawatt (MW). The above indicators are linearly combined as follows to obtain the evaluation indicators of the primary and secondary frequency regulation capabilities of various regulation resources. R=α1t res +α2P cmax +α3P cmin +α4σ+α5K+α6θ+α7t con +α8P b In the formula: t res For response time, P cmax For maximum regulation capacity, P cmin For minimum adjustment capability, σ is the overshoot, θ is the phase delay, and t con For continuous frequency modulation capability, P b This is for backup capacity.

3. The method for dynamic quantitative evaluation of primary and secondary frequency regulation capabilities of a power grid according to claim 1, characterized in that: The model for predicting the primary and secondary frequency regulation capabilities of the power grid at their respective time scales, based on the characteristic indicators of the primary and secondary frequency regulation capabilities of the power grid, is as follows: R=α1t res +α2P cmax +α3P cmin +α4σ+α5K+α6θ+α7t con +α8P b Where, α i The weight of each factor in the frequency modulation system depends on the characteristics of various regulation resources and the frequency modulation work involved.

4. The method for dynamic quantitative evaluation of primary and secondary frequency regulation capabilities of a power grid according to claim 1, characterized in that: The method involves predicting the primary and secondary frequency regulation capabilities of various power plants in real time when the power grid experiences frequency fluctuations, based on a prediction model, and determining the participation degree of various regulating power plants when the power grid experiences frequency fluctuations. The frequency regulation capability R of various regulation resources is calculated using a predictive model. i Assign primary and secondary frequency modulation tasks, according to R i Determine the participation β of various regulating power plants when grid frequency fluctuations occur. i :

5. The method for dynamic quantitative evaluation of primary and secondary frequency regulation capabilities of a power grid according to claim 1, characterized in that: The establishment of an energy storage-assisted frequency regulation model based on the participation of various regulating power plants includes: Define ΔP as the power deficit of the system after frequency regulation by thermal power, wind power, photovoltaic power, and hydropower: Where P is the power required for frequency modulation, P i Useful power contributed to thermal power, wind power, photovoltaic power and hydropower; the contribution of hydropower can be positive or negative; The energy storage-assisted frequency regulation model is divided into frequency regulation charging mode and frequency regulation discharging mode based on ΔP: If ΔP>0, the energy storage auxiliary system is in frequency regulation charging mode; If ΔP < 0, the energy storage auxiliary system is in frequency modulation discharge mode.

6. The method for dynamic quantitative evaluation of primary and secondary frequency regulation capabilities of a power grid according to claim 1, characterized in that: The method combines the predictive model, the energy storage-assisted frequency regulation model, and the traditional frequency response model to obtain a power grid frequency response model that integrates multiple regulation resources and considers spatiotemporal and meteorological factors, including: Each frequency regulation unit model is a combination of the corresponding traditional frequency response model, prediction model, and participation degree to obtain a frequency response model for dynamic quantitative evaluation of the primary and secondary frequency regulation capabilities of the power grid. The frequency response model of a traditional thermal power unit is: Among them, F HP T is the mechanical power proportionality coefficient of the cylinder. CH T RH Let be the volumetric effect time constant of the cylinder, and s be the transfer function variable; The traditional frequency response model for wind power units is: Among them, T ω k is the inertial response time constant. df ΔP is the inertial response coefficient. ω Δf is the change in inertial control power; k is the change in power system frequency. pf T is the primary frequency modulation coefficient. β Variable pitch response time constant; The traditional frequency response model for hydropower units is: Among them, T wa The inertial time constant of the water flow in the hydroelectric generator unit; The traditional frequency response model of a photovoltaic frequency modulation unit is: G pv (s)=(k pv C b U dczero +H pv )s Where, k pv For photovoltaic control parameters, H pv C is the virtual inertia time constant. b For high voltage DC capacitors, U dczero This is the initial value of the high voltage DC voltage; The traditional energy storage frequency response model is: Where, k E Battery gain, T E It is a specific timeframe for how battery energy storage can sustain its function. Incorporate a secondary frequency modulation model; If a single-zone frequency modulation uses a fixed-frequency control mode, and if two zones are interconnected, the above TBC-TBC mode is used. The ACE calculation formula is as follows: ACE = -B × Δf ACE=ΔP 12 +B×Δf Where B is the frequency ratio deviation coefficient, P is the actual power on all tie lines in the two control zones, and the secondary frequency modulation task at the corresponding time is determined based on the sign of the ACE value.

7. The method for dynamic quantitative evaluation of primary and secondary frequency regulation capabilities of a power grid according to claim 1, characterized in that: Based on the power grid frequency response model, evaluation indicators for the primary and secondary frequency regulation capabilities of the entire system are established to dynamically and quantitatively assess the primary and secondary frequency regulation capabilities of the system, including: Establish evaluation indicators for the frequency regulation capability of the entire power grid: Where Δf0 is the rate of change of frequency, Δf max For the maximum frequency deviation, Δf s For steady-state frequency deviation, K L k1, k2, and k3 are proportionality coefficients; The primary and secondary frequency regulation capabilities of the system are dynamically and quantitatively evaluated using evaluation indicators and power grid frequency response models. The frequency stability of the entire system is examined using the rate of change of frequency, maximum frequency deviation, and steady-state frequency deviation as boundary indices. The frequency stability indices of the system are analyzed, and the frequency modulation-related parameters are theoretically analyzed from the frequency domain perspective, as shown in the following equation: In the above formula, K L This is the gain coefficient, used for ease of calculation and plotting.

8. A dynamic quantitative evaluation device for the primary and secondary frequency regulation capabilities of a power grid, characterized in that: include: The characteristic index establishment module is used to establish characteristic indicators of the primary and secondary frequency regulation capabilities of the power grid based on the characteristics of frequency regulation of thermal power and frequency regulation of wind power, photovoltaic power, hydropower, and energy storage. The prediction module is used to establish models for predicting the primary and secondary frequency regulation capabilities of the power grid at their respective time scales based on the characteristic indicators of the primary and secondary frequency regulation capabilities of the power grid. The participation determination module is used to predict the primary and secondary frequency regulation capabilities of various power plants in real time when the power grid experiences frequency fluctuations, based on the prediction model, and to determine the participation of various regulating power plants when the power grid experiences frequency fluctuations. The energy storage-assisted frequency regulation model establishment module is used to establish an energy storage-assisted frequency regulation model based on the participation of power plants in various regulatory resources. The power grid frequency response model building module is used to combine the prediction model, the energy storage-assisted frequency regulation model, and the traditional frequency response model to obtain a power grid frequency response model that integrates multiple regulation resources and considers spatiotemporal and meteorological factors. The evaluation module is used to establish evaluation indicators for the primary and secondary frequency regulation capabilities of the entire system based on the power grid frequency response model, and to dynamically and quantitatively evaluate the primary and secondary frequency regulation capabilities of the system.

9. The dynamic quantitative evaluation device for primary and secondary frequency regulation capabilities of a power grid according to claim 8, characterized in that: The model for predicting the primary and secondary frequency regulation capabilities of the power grid at their respective time scales, based on the characteristic indicators of the primary and secondary frequency regulation capabilities of the power grid, is as follows: R=α1t res +α2P cmax +α3P cmin +α4σ+α5K+α6θ+α7t con +α8P b Where, α i The weight of each factor in the frequency modulation system depends on the characteristics of various regulation resources and the frequency modulation work involved.

10. A computer device, comprising a storage medium, a processor, and a computer program stored on the storage medium and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the dynamic quantitative evaluation method for primary and secondary frequency regulation capabilities of a power grid as described in any one of claims 1-7.

11. A computer storage medium, characterized in that: The computer storage medium contains a computer program, which, when executed by a processor, implements the steps of a dynamic quantitative evaluation method for the primary and secondary frequency regulation capabilities of a power grid as described in any one of claims 1-7.

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