Power system dispatching method considering inertia and power adequacy

Abstract

The application belongs to the field of power grid dispatching, and specifically discloses a power system dispatching method considering inertia and power double adequacy. The application realizes quantitative characterization of dynamic characteristics of inertia resources on the load side by establishing a frequency response model containing load power variation, and accordingly deduces inertia adequacy constraints of dynamic capabilities on both sides of the coordinated source and load and power adequacy constraints of fused load power support. Finally, in the optimization dispatching, the implicit reserve on the load side is converted into a dispatchable explicit resource, breaking through the problem of limited regulation potential caused by ignoring the dynamic support of the load in the traditional dispatching model, while ensuring the stability of the frequency and significantly improving the system operation and dispatching performance.

Description

Technical Field

[0001] This application belongs to the field of power grid dispatching, and more specifically, relates to a power system dispatching method that takes into account both inertia and power adequacy. Background Technology

[0002] As the grid connection rate of new energy sources, such as wind and solar power, continues to rise, the inertia level of the power system is declining. Meanwhile, the uncertainty of wind and solar power output leads to an increased demand for inertia to support frequency stability. Therefore, when formulating dispatch plans for unit combinations, considering both system power sufficiency and inertia sufficiency to support the frequency stability of the new power system is the future development direction.

[0003] Existing research mainly optimizes unit start-up and shutdown states and reserve capacity by introducing frequency safety indicators to ensure that the system has sufficient frequency regulation capability to maintain frequency stability. Currently, commonly used frequency safety indicators include the maximum rate of frequency change after the system is disturbed, the lowest frequency point, and the quasi-steady-state frequency. By considering the entire process of dynamic frequency changes in the system, frequency safety indicator constraints can be established using time-frequency domain analytical transformation. Incorporating these constraints into the unit combination model can effectively improve the system's frequency response capability and stability.

[0004] However, current research on generator configuration issues considering frequency security mainly focuses on utilizing the inertia of generators on the power supply side to improve system frequency response, while insufficiently considering the role of load-side inertia, thus failing to achieve coordinated frequency regulation between the source and load. Studies have shown that load-side resources (such as industrial loads and temperature-controlled loads) can provide crucial dynamic frequency support for the system due to their rapid response characteristics. In fact, the power support characteristics of load-side inertia resources are equivalent to providing the system with "implicit reserve capacity," capable of dynamically compensating for power deficits and suppressing frequency fluctuations.

[0005] In summary, the current power system lacks specific means of dynamically representing load-side inertia and fails to fully consider the dynamic support capability of inertia during the frequency regulation process of sources and loads. As a result, the system's frequency response potential has not been fully realized, which restricts the improvement of power system frequency stability and thus limits power system dispatch. Summary of the Invention

[0006] In view of the shortcomings of the prior art, the purpose of this application is to provide a power system dispatching method that takes into account both inertia and power adequacy, aiming to solve the technical problem that the current power system lacks a means of dynamically representing the load-side inertia, which leads to the limitation of power system dispatching.

[0007] To achieve the above objectives, in a first aspect, this application provides a power system dispatching method that considers both inertia and power adequacy, comprising: determining a system frequency response model based on disturbance power, load power change, prime mover power change, and equivalent inertia time constant; obtaining inertia adequacy constraints and quasi-steady-state frequency deviation based on the system frequency response model; obtaining power adequacy constraints based on disturbance power and quasi-steady-state frequency deviation; and outputting a dispatching scheme based on the system objective function, according to the inertia adequacy constraints, power adequacy constraints, and system operation constraints.

[0008] In one embodiment, the system frequency response model is as follows:

[0009] ;

[0010] ;

[0011] ;

[0012] ;

[0013] ;

[0014] ;

[0015] in, This refers to the system frequency deviation. For time; The disturbance power; This refers to the unit's droop coefficient; The equivalent inertial time constant of the unit; The power coefficient of the high-pressure cylinder of the prime mover; This is the load frequency regulation effect coefficient; The mechanical power factor; This is the unit's reheat time constant; For the natural oscillation angular frequency, For damping ratio, For damped oscillation angular frequency, and These are intermediate variables in the calculation process.

[0016] In one embodiment, the inertia adequacy constraint includes: a maximum frequency change rate constraint, a minimum frequency point constraint, and a quasi-steady-state frequency deviation constraint. Obtaining the inertia adequacy constraint and the quasi-steady-state frequency deviation based on the system frequency response model includes: obtaining the descent slope of the system frequency response model at the start of the disturbance as the maximum frequency change rate, and establishing a maximum frequency change rate constraint; obtaining the time when the descent slope of the system frequency response model is 0 as the time when the frequency reaches its minimum point, obtaining the minimum frequency point based on the time when the frequency reaches its minimum point, and establishing a minimum frequency point constraint; obtaining the sustained frequency deviation after the system frequency response model enters the quasi-steady state as the quasi-steady-state frequency deviation, and establishing a quasi-steady-state frequency deviation constraint.

[0017] In one embodiment, obtaining power adequacy constraints based on disturbance power and quasi-steady-state frequency deviation includes: obtaining the reserve capacity of the power system and the disturbance power of load, wind power output and photovoltaic output; performing probabilistic quantization characterization on the disturbance power to obtain disturbance power constraints; and obtaining power adequacy constraints based on disturbance power, reserve capacity, quasi-steady-state frequency deviation and disturbance power constraints.

[0018] In one embodiment, the disturbance power is probabilistically quantified to obtain disturbance power constraints, including: obtaining prediction error variables of power system load, wind power output, and photovoltaic power output; constraining the disturbance power based on the prediction error variables and a preset confidence level, and obtaining a probability distribution model of the prediction error as the disturbance power constraint.

[0019] In one embodiment, based on inertia adequacy constraints, power adequacy constraints, and system operation constraints, a scheduling scheme is output according to the system objective function, including: obtaining an upper-level objective considering power adequacy for unit combination optimization and a lower-level objective considering inertia adequacy verification based on the system objective function; solving the upper-level objective to obtain the system's decision variables and inputting them into the lower-level objective; generating an optimized cut and inputting it into the upper-level objective when the decision variables do not satisfy the lower-level objective, and performing the steps of solving the upper-level objective to obtain the system's decision variables and inputting them into the lower-level objective; and outputting the decision variables as a scheduling scheme when the decision variables satisfy the lower-level objective.

[0020] Secondly, this application provides a power system dispatching device that considers both inertia and power adequacy, comprising: a model building module for determining a system frequency response model based on disturbance power, load power change, prime mover power change, and equivalent inertial time constant; a constraint acquisition module for acquiring inertia adequacy constraints and quasi-steady-state frequency deviation based on the system frequency response model; and for acquiring power adequacy constraints based on disturbance power and quasi-steady-state frequency deviation; and a dispatching output module for outputting a dispatching scheme based on inertia adequacy constraints, power adequacy constraints, and system operation constraints, according to the system objective function.

[0021] Thirdly, this application provides an electronic device, comprising: at least one memory for storing a program; and at least one processor for executing the program stored in the memory, wherein when the program stored in the memory is executed, the processor is configured to execute the method described in the first aspect or any possible implementation thereof.

[0022] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to perform the method described in the first aspect or any possible implementation thereof.

[0023] Fifthly, this application provides a computer program product that, when run on a processor, causes the processor to perform the method described in the first aspect or any possible implementation thereof.

[0024] Overall, the technical solutions conceived in this application have the following beneficial effects compared with the prior art:

[0025] This application achieves a quantitative characterization of the dynamic characteristics of load-side inertia by incorporating load power variation into the frequency response model. This step overcomes the limitation of traditional models that rely solely on power supply parameters, establishing a complete analytical framework for system frequency characteristics that includes load-side dynamic response.

[0026] Next, based on the inertia adequacy constraint derived from the above model, the dynamic support capacity of the load side is transformed into mathematical constraints through the quasi-steady-state frequency deviation index. This step solves the key problem that load-side inertia cannot participate in scheduling due to the lack of dynamic representation, ensuring that the system inertia constraint simultaneously covers resources on both the source and load sides. Power adequacy constraints are obtained based on disturbance power and quasi-steady-state frequency deviation. The reduction in quasi-steady-state frequency deviation directly reflects the power deficit caused by the reduced load response, making the new power constraint implicitly include the equivalent reserve contribution of the load side, transforming the implicit reserve provided by load resources into an explicit power constraint. This step, through the coordinated optimization of load-side power support capacity and conventional power reserve, breaks through the limitation of traditional power constraints relying solely on unit reserves, reducing redundant reserve configurations.

[0027] Finally, by combining the aforementioned dual abundance constraints and system operation constraints for optimization, and since the load-side inertia resources participate in the optimization scheduling in the form of dynamic constraints, the system operation scheduling performance is ultimately improved while ensuring frequency security. Attached Figure Description

[0028] Figure 1 This is one of the flowcharts provided in the embodiments of the power system dispatching method that takes into account both inertia and power adequacy in this application;

[0029] Figure 2This is a system frequency response model diagram provided in the embodiments of the method of this application;

[0030] Figure 3 This is a graph showing the relationship between inertia, frequency, and power provided in the embodiments of the method of this application;

[0031] Figure 4 This is the second flowchart provided in the embodiments of the method of this application;

[0032] Figure 5 This is the third flowchart provided in the embodiments of the method of this application;

[0033] Figure 6 This is the fourth flowchart provided in the embodiments of the method of this application;

[0034] Figure 7 This is a flowchart of the optimization model solution provided in the embodiments of the method of this application;

[0035] Figure 8 This is a load and renewable energy forecast curve provided in the embodiments of the method of this application;

[0036] Figure 9 These are inertial time constant diagrams for different time periods under different schemes provided in the embodiments of the method of this application;

[0037] Figure 10 This is a graph showing the maximum frequency change rate for different time periods under different schemes provided in the embodiments of the method of this application;

[0038] Figure 11 These are diagrams illustrating the start-up and shutdown status of generating units at different times under different schemes provided in the embodiments of the method of this application;

[0039] Figure 12 These are the lowest frequency points in different time periods under different schemes provided in the embodiments of the method of this application;

[0040] Figure 13 These are backup capacity diagrams for different time periods under different schemes provided in the embodiments of the method of this application;

[0041] Figure 14 A schematic diagram of the module structure provided for an embodiment of the power system dispatching device that takes into account both inertia and power redundancy in this application;

[0042] Figure 15 This is a schematic diagram of the structure provided in the embodiment of the electronic device of this application. Detailed Implementation

[0043] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0044] Current unit combination models considering frequency security mainly rely on the generator inertia on the power supply side to support the system frequency, but fail to effectively characterize and utilize load-side resources, such as the dynamic inertia support capacity of industrial / temperature-controlled loads. Due to the lack of dynamic modeling methods for load-side inertia, system scheduling cannot quantify the role of "hidden reserve capacity" in load resource response to power deficits and suppression of frequency fluctuations. As a result, the potential of source-load coordinated frequency regulation is not fully released, limiting the improvement of frequency stability and scheduling flexibility.

[0045] Based on this, this application proposes a power system dispatching method that considers both inertia and power adequacy. First, the necessity of considering both inertia and power adequacy in unit combination in this application is explained.

[0046] With the construction and development of new power systems, the power balance between system sources and loads has undergone significant changes. On the one hand, the power balance relationship of the power system has shifted from deterministic matching to probabilistic balance; on the other hand, the continuous decline in the proportion of synchronous generator units has led to a gradual decay of the system's equivalent inertia, posing a risk of failure to the traditional frequency stabilization mechanism that relies on the inertia support of synchronous generator units. This dual dilemma of increased random disturbances and weakened inertia support makes it impossible for traditional generator unit combination models that rely solely on rigid power balance to guarantee power adequacy or maintain the dynamic safety boundary of the system frequency.

[0047] This application proposes a power system dispatching method that considers both inertia and power adequacy. Focusing on the "inertia-frequency-power" relationship under source-load coordinated frequency regulation, it proposes a unit combination optimization model that integrates both "inertia-power" adequacy. Based on the traditional system frequency response model relying on thermal power unit frequency regulation, this application considers load frequency response, constructs a source-load coordinated frequency regulation frequency response model, analyzes the source-load "inertia-frequency-power" relationship, and incorporates dynamic frequency safety constraints into the unit combination model: On the one hand, utilizing the power-frequency characteristics of the load, a controllable steady-state frequency deviation is allowed, thereby releasing the frequency regulation potential on the load side and balancing disturbance power together with unit reserves, forming a system power adequacy constraint; on the other hand, a source-load frequency regulation system frequency response model is established to analyze the "inertia-frequency-power" relationship. Based on the frequency change rate and the expression for the frequency minimum point, an inertia adequacy constraint is constructed to ensure system frequency safety.

[0048] Therefore, please refer to Figure 1 , Figure 1 This is one of the flowcharts provided in the embodiment of the power system dispatching method considering both inertia and power abundance of this application. In this embodiment, the power system dispatching method considering both inertia and power abundance includes steps S10 to S40.

[0049] Step S10: Determine the system frequency response model based on the disturbance power, load power change, prime mover power change, and equivalent inertial time constant.

[0050] It should be noted that obtaining the System Frequency Response Model (SFRM) is a core task in power system analysis, planning, and control, and its necessity stems from the attribute of frequency as a key indicator of system operation. Here, this embodiment aims to obtain the system frequency response model for source and load frequency regulation.

[0051] It should be noted that the system frequency response model for source-load frequency regulation is a core tool for analyzing the dynamic process of frequency regulation jointly participated in by power sources and loads in a power system. Its goal is to quantify the frequency response behavior of power sources and loads under power imbalance, reveal the laws governing system frequency changes, and provide theoretical support for the design of frequency regulation control strategies. The system frequency response model for source-load frequency regulation must simultaneously consider the frequency regulation capability on the power source side and the dynamic characteristics on the load side, such as the load frequency regulation effect.

[0052] It is understandable that the frequency response of a power system is essentially a dynamic process of power imbalance, frequency change, frequency regulation resource activation, and frequency recovery. The four parameters selected in this application directly correspond to the key aspects of this process, fully covering the frequency response. Disturbance power represents the initial power deficit or surplus of the system and is the triggering source of the frequency response. Clearly defining the magnitude and time-varying characteristics of the disturbance power is fundamental to analyzing the initiation conditions of frequency changes. Load power change reflects the dynamic response of the load side to frequency changes, i.e., the load frequency regulation effect, reflecting the load's adaptive capability during frequency fluctuations, such as the automatic load reduction of an induction motor as the frequency decreases. This parameter is directly related to the system's inherent frequency regulation potential. Prime mover power change represents the active frequency regulation capability achieved by the power supply side through speed governors or Automatic Generation Control (AGC), and is the main control means for restoring frequency stability. Its dynamic characteristics directly affect the speed and accuracy of frequency recovery. The equivalent inertial time constant measures the system's resistance to frequency changes and is jointly determined by the rotating mass of the synchronous generator (traditional power supply) and the virtual inertia of the power electronic interface power supply. The magnitude of the inertial time constant directly determines the initial rate of frequency change and is a key indicator for evaluating the system's inertia level.

[0053] It should be noted that, based on the static frequency characteristics of the load, when the system frequency changes from the rated value... Fluctuation to When the load power changes, the corresponding change is given by the following formula (1).

[0054] (1)

[0055] In the formula, For frequency equal to The active load of the system at that time; For a load proportional to frequency The share it occupies; This is for frequency deviation; This is denoted as the load frequency regulation effect coefficient. The load component that participates in the frequency response.

[0056] Further construct a system frequency response model that considers source and load frequency modulation, such as Figure 2 As shown. Figure 2 middle, The disturbance power is mainly caused by uncertainties in wind power, photovoltaic power, and load. The equivalent inertial time constant of the unit; This refers to the unit's damping coefficient; This refers to the unit's droop coefficient; This is the unit's reheat time constant; The power coefficient of the high-pressure cylinder of the prime mover; The mechanical power factor; This is the load frequency regulation effect coefficient.

[0057] It should be noted that, in order to simplify the functional relationship, this application does not consider the damping coefficient of the thermal power unit. The equivalent inertial time constant is related to the inertial time constant and start-up and shutdown state of each unit, i.e., the following formula (2).

[0058] (2)

[0059] In the formula, N This represents the total number of generating units. For the unit i The inertial time constant; For the unit i Rated capacity; This is the system's baseline capacity; This represents the start / stop state of unit i at time t. At this time, according to... Figure 3 By using Laplace transform or piecewise solution, the time-domain expression of the frequency response model of the source and load frequency modulation system with respect to the system inertia, frequency and power can be obtained, which serves as the expression of the system frequency response model of this application, namely the following equation (3).

[0060] (3)

[0061] in, For the natural oscillation angular frequency, For damping ratio, For damped oscillation angular frequency, and These are intermediate variables in the calculation process. The expressions for each variable are as follows (4).

[0062] (4)

[0063] Based on equations (3) and (4), typical values ​​of inertial time constant and disturbance power were selected to explore the intrinsic correlation mechanism between inertia, frequency, and power. The results are as follows: Figure 3 As shown, Figure 3 This is an analysis diagram of the relationship between inertia, frequency, and power provided in the embodiments of the method of this application. It can be seen that as the disturbance power increases, the amplitude of the system frequency change intensifies significantly, and the frequency drop depth further increases, demonstrating the direct correlation between the power disturbance intensity and the frequency dynamic response; the larger the inertial time constant, the stronger the system's ability to suppress frequency fluctuations and the smaller the frequency change rate, indicating that the inertia level is a key factor in suppressing rapid frequency drops.

[0064] Therefore, the system frequency response model proposed in this embodiment conforms to natural laws, can well characterize the relationship between inertia, frequency and power, and includes the dynamic characterization of load-side resources, thus improving the potential of the system frequency response model.

[0065] Step S20: Obtain the inertia adequacy constraint and quasi-steady-state frequency deviation based on the system frequency response model.

[0066] It is important to note that frequency security is a core element in ensuring grid stability and power supply reliability during power system operation. When the system is subjected to power disturbances (such as generator tripping, sudden load increases, etc.), the frequency undergoes a dynamic change process, and its key indicators directly affect whether the system can quickly recover stability. Inertia adequacy constraint modeling quantifies the system's ability to support frequency changes through inertia, and requires careful consideration of the maximum rate of frequency change, the lowest frequency point, the quasi-steady-state frequency deviation, and the constraint modeling method.

[0067] It should be noted that the maximum rate of frequency change reflects the system's ability to buffer sudden power fluctuations due to inertia. Greater inertia results in smoother frequency changes; insufficient inertia may lead to a sharp drop in frequency, triggering malfunctions in protection devices or damaging equipment. The lowest frequency point determines whether the system enters an emergency state; if it falls below a critical value, it may cause a cascading failure. The quasi-steady-state frequency deviation reflects the system's secondary frequency regulation capability, such as the AGC response speed; excessive deviation may affect the normal operation of user equipment or cause inter-regional power oscillations.

[0068] In this embodiment, the inertia adequacy constraint is obtained based on the system frequency response model because it can quantify the coupling relationship between system inertia, frequency regulation resources, and frequency dynamic characteristics, thereby providing theoretical support for the frequency security assessment of high-proportion renewable energy power grids. Specifically, the expression of the system frequency response model can be substituted into the expression of the aforementioned frequency security index. The core logic lies in directly linking frequency dynamic behavior with system parameters through mathematical derivation, thereby quantifying the constraining effect of inertia on frequency security.

[0069] It is understood that this embodiment provides a specific implementation method for obtaining inertia adequacy constraint modeling, such as... Figure 4 As shown, Figure 4 This is the second flowchart provided in the method embodiment of this application.

[0070] In this embodiment, step S20 includes steps S21 to S23.

[0071] Step S21: Obtain the descent slope of the system frequency response model at the start of the disturbance as the maximum frequency change rate, and establish a maximum frequency change rate constraint.

[0072] It should be noted that the maximum frequency change rate The slope of the initial frequency drop during the disturbance is represented by the following expression (5), and the maximum frequency change rate should be less than the upper limit value.

[0073] (5)

[0074] Step S22: Obtain the time when the slope of the system frequency response model is 0 as the time when the frequency reaches the lowest point, obtain the lowest frequency point based on the time when the frequency reaches the lowest point, and establish a minimum frequency point constraint.

[0075] It should be noted that the lowest frequency point It is the lowest frequency reached before frequency recovery. The time when the frequency reaches its lowest point can be obtained. The formula is (6). The lowest frequency point can then be calculated, expressed as (7). The lowest frequency point should be greater than the lower limit.

[0076] (6)

[0077] (7)

[0078] Step S23: Obtain the sustained frequency deviation of the system frequency response model after it enters the quasi-steady state as the quasi-steady-state frequency deviation, and establish the quasi-steady-state frequency deviation constraint.

[0079] It should be noted that the quasi-steady-state frequency deviation The sustained frequency deviation after the system enters the quasi-steady state is calculated using the following formula (8).

[0080] (8)

[0081] It should be noted that by setting limits on the obtained frequency safety indicators according to the system operation requirements, dynamic inertia adequacy constraints can be constructed as frequency safety constraints.

[0082] Step S30: Obtain power adequacy constraints based on disturbance power and quasi-steady-state frequency deviation.

[0083] Understandably, in the power system frequency security assessment, the core of step S30 is to deduce the power adequacy constraints required by the system through the correlation between disturbance power, i.e., the power deficit or surplus suffered by the system and the quasi-steady-state frequency deviation, i.e. the minimum reserve capacity or frequency regulation resources that the system needs to reserve, so as to ensure that the system can control the frequency deviation within a safe range after the disturbance occurs.

[0084] It should be noted that, considering the frequency regulation power support of the load, this application takes the reserve constraint that takes into account the frequency regulation effect of the source and load as the power adequacy constraint, and the system reserve capacity must meet the regulation requirements of both the power supply side and the load side.

[0085] Furthermore, this embodiment provides a specific implementation method for obtaining power adequacy constraint modeling, such as... Figure 5 As shown, Figure 5 This is the third flowchart provided in the method embodiment of this application.

[0086] In this embodiment, step S30 includes steps S31 to S33.

[0087] Step S31: Obtain the reserve capacity of the power system and the disturbance power of the load, wind power output and photovoltaic power output.

[0088] It should be noted that reserve capacity refers to redundant resources reserved by the system to cope with sudden power shortages. It is usually divided into spinning reserve, which is the capacity of units that are connected to the grid and can respond quickly, and non-spinning reserve, which is the resources that need to be started or dispatched, such as energy storage and interruptible loads. Acquisition methods include: obtaining daily / weekly dispatch plans from the power grid dispatch center, which clearly indicate the planned output and reserved reserve capacity of each unit; or collecting the unit operating status, output limit and current available capacity in real time through the dispatch automation system; in the power market environment, reserve capacity may be procured through the ancillary services market; and calculating the required reserve capacity based on historical data or predictive models through probabilistic security assessments.

[0089] It should be noted that load disturbances refer to sudden increases or decreases in load power, which may be caused by industrial start-ups and shutdowns, sudden changes in air conditioning load, etc. Disturbances in wind power output and solar power output typically refer to disturbances significantly affected by weather, manifesting as sudden drops or increases in output, such as a precipitous drop in solar power output due to cloud cover. The disturbance power of load, wind power output, and solar power output can be collected in real time by deploying synchronous phasor measurement units to calculate the power change rate; combined with anemometers and power curves, anomalies where wind speed changes suddenly occur but output does not follow can be analyzed; and irradiance sensors and sky imagers can be used to capture cloud movement to predict precipitous drops in output.

[0090] Step S32: Perform probabilistic quantization to characterize the disturbance power and obtain the disturbance power constraint.

[0091] It should be noted that by quantifying the uncertainty of disturbance power through probabilistic statistical methods, such as the probability distribution of sudden load changes and sudden drops in wind power output, probabilistic disturbance constraints that meet system safety requirements are generated to replace traditional deterministic constraints, such as fixed thresholds, thereby improving the economy and robustness of reserve capacity configuration.

[0092] Specifically, the disturbance power is probabilistically quantified and characterized, and the disturbance power constraints are obtained by: obtaining the prediction error variables of power system load, wind power output and photovoltaic power output; constraining the disturbance power based on the prediction error variables and the preset confidence level, and obtaining the probability distribution model of the prediction error as the disturbance power constraint.

[0093] Understandably, the prediction error variable, which is the difference between the actual and predicted values, needs to be defined differently for different types of disturbances. By collecting a large amount of historical data and performing statistical analysis on the prediction error variable, its probability distribution model can be obtained. For example, the prediction errors for load, wind power, and photovoltaic output may follow other types of distributions, such as normal or Laplace distributions. These probability distribution models will serve as disturbance power constraints for subsequent power system analysis and reserve capacity configuration.

[0094] It is understandable that, since the prediction errors of load, wind power and photovoltaic output are random, the disturbance power is no longer a constant. Therefore, this implementation method uses the p-effective point method to perform probability quantification characterization as a disturbance power constraint, as shown in equations (9) to (11).

[0095] (9)

[0096] (10)

[0097] (11)

[0098] In the formula, Pr{} is the mathematical symbol for probability quantification; , , These represent the disturbance power caused by errors in load, wind power, and photovoltaic output prediction, respectively. , , These are the prediction error variables for load, wind power output, and photovoltaic output, respectively. , , For the corresponding preset credit level.

[0099] Understandably, the confidence level is determined based on the power system's security requirements and operational strategies. For example, in scenarios with high security requirements, a confidence level of 95% or 99% might be chosen. Different confidence levels correspond to different risk tolerances; a higher confidence level means a higher degree of protection for system security, but it may also lead to an increase in resources such as required reserve capacity. For a given confidence level, the corresponding quantiles can be calculated based on the probability distribution model of the prediction error. Taking the normal distribution as an example, to calculate the one-sided lower or upper confidence limit for a preset confidence level, the quantile table or correlation function of the standard normal distribution can be used. There are also methods for calculating quantiles for other distribution types. For example, for the Laplace distribution, the quantiles at a given confidence level can be calculated using the properties of its probability density function and cumulative distribution function, thereby obtaining the deterministic equivalent disturbance power.

[0100] Step S33: Based on the disturbance power, reserve capacity, quasi-steady-state frequency deviation, and disturbance power constraint, obtain the power adequacy constraint.

[0101] Based on the above, power adequacy constraints can be established based on disturbance power, reserve capacity, and quasi-steady-state frequency deviation, as shown in equation (12).

[0102] (12)

[0103] in, For the unit i The reserve capacity and the disturbance power should meet the disturbance power constraints of equations (9) to (11) above. The left side of the equation represents the available power regulation capability of the system. Among them, the first term on the left is the sum of the reserve capacity of all units, which can directly increase or decrease the power generation to cope with the disturbance. The second term on the left reflects the regulation effect of the load on frequency changes under quasi-steady state. When the frequency drops, the load power will automatically decrease, thereby helping the system to restore power balance to a certain extent.

[0104] It should be noted that the right side of the equation represents the total power disturbance faced by the system, including random changes in load and uncertain fluctuations in wind and solar power output. This formula requires that the system's available power regulation capacity be greater than or equal to the total power disturbance faced, in order to ensure that the system can maintain stable operation after a disturbance occurs and avoid situations where power deficits lead to a significant drop or rise in frequency.

[0105] Step S40: Based on inertia adequacy constraints, power adequacy constraints, and system operation constraints, output a scheduling scheme according to the system objective function.

[0106] It is understandable that, considering the operating costs and standby costs of thermal power units, the objective function is to achieve economic optimization, as shown in equation (13).

[0107] (13)

[0108] in, , , This is the power generation cost coefficient of the generating unit; For the unit i During the period t contribution; Let be the reserve capacity of unit i at time t; The start / stop state of unit i at time t; This represents the start-up and shutdown status of unit i at time t-1; , For the unit i During the period t Start-up and shutdown cost coefficients; For the unit i The backup cost coefficient. Combining the inertia adequacy constraint, power adequacy constraint and system operation constraint, the specific expression is as follows (14).

[0109] (14)

[0110] It should be noted that the first formula in equation (14) is a power balance constraint. , , The first formula represents the predicted values ​​of wind power output, photovoltaic power output, and system load for time period t; the second formula represents the unit ramp-up constraint. , The units i The upward and downward ramp rates; the third formula represents the unit output constraint. , The units i The upper and lower limits of output; the fourth formula is the minimum start-up and shutdown time constraint for the unit. , The units i exist t Power on / off times; , The units i Minimum power-on and minimum power-off times.

[0111] It should be noted that the fifth formula in equation (14) is a power adequacy constraint. This represents the total disturbance power of the system. , The confidence level that the set system reserve capacity needs to meet; formulas six and seven are constraints on inertia adequacy. and It can be calculated using equations (2) and (6) respectively. For the limit of the rate of change of frequency, This is the minimum frequency limit.

[0112] Understandably, after obtaining the objective function and constraints, optimization software, such as MATLAB's optimization toolbox or the Gurobi solver, is used to solve the established mathematical model. The solution results will provide information such as the output plan and start-up / shutdown status of each unit, i.e., the scheduling scheme. The solution results are analyzed to check whether all constraints are met, and the economy and feasibility of the scheduling scheme are evaluated. If the results are unsatisfactory, the model parameters or constraints need to be adjusted, and the solution needs to be repeated.

[0113] Furthermore, this embodiment provides an implementation method for a specific output scheduling scheme; please refer to [link / reference]. Figure 6 and Figure 7 , Figure 6 This is the fourth flowchart provided in the embodiments of the method of this application; Figure 7 This is a flowchart of the optimization model solution provided in the method embodiment of this application.

[0114] In this embodiment, step S40 includes steps S41 to S44.

[0115] Step S41: Based on the system objective function, obtain the upper-level objective of unit combination optimization considering power adequacy and the lower-level objective of inertia adequacy verification.

[0116] It should be noted that the proposed unit combination model, i.e., the system objective function, is decomposed into an upper-level main problem / upper-level objective and a lower-level sub-problem / lower-level objective, connected by a Benders cut. The upper-level objective focuses on the power adequacy of this paper, solving the problem of outputting the unit start-up and shutdown states of the unit combination model. The lower-level objective, addressing the inertia adequacy requirement, verifies the frequency safety constraint satisfaction of the above scheme under disturbance scenarios.

[0117] It should be noted that Benders cut transmits information from lower-level subproblems to the upper-level master problem in the form of linear constraints. This allows the master problem to consider feedback from lower-level subproblems in subsequent iterations, thereby optimizing the solution process. Through continuous iteration, the master problem adjusts the unit combination scheme according to Benders cut, and the lower-level subproblems verify the adjusted scheme until an economically optimal unit combination scheme that simultaneously satisfies the constraints of sufficient power and sufficient inertia is found. This method can efficiently solve unit combination optimization problems that consider both sufficient power and sufficient inertia.

[0118] Step S42: Solve the upper-level objective to obtain the system's decision variables and input them into the lower-level objective.

[0119] It should be noted that the upper-level objective is a unit combination optimization problem that considers power adequacy, typically a mixed-integer linear programming problem because it involves the start-up and shutdown states of the units (discrete variables) and their output (continuous variables). Solvers such as Gurobi, MATLAB, or CPLEX can be directly called. The built-in branch and cut algorithm combines branch and bound with the cutting plane, leveraging the convexity of linear problems to guarantee convergence to the global optimum.

[0120] It should be noted that after the upper-level objective optimization converges, the start-up and shutdown status, output, and standby status of each unit can be obtained as decision variables, and the obtained optimization solution can be used as the input of the lower-level objective.

[0121] Understandably, the start-up and shutdown status of the generating units clearly defines their operating or shutdown status within the scheduling cycle. This is crucial for lower-level inertia adequacy verification, as only operating units can provide rotational inertia. Unit output includes the actual output values ​​of each unit at different times, affecting the system's total inertia calculation and frequency dynamic response. Reserve status includes spinning reserve and non-spinning reserve; the allocation of reserve capacity affects the system's power balance and frequency stability under disturbances. Lower-level subproblems need to consider this reserve information to assess the system's inertia adequacy.

[0122] Step S43: When the decision variables do not satisfy the lower-level objective, generate an optimal cut input to the upper-level objective, and execute the step of solving the upper-level objective to obtain the system's decision variables and inputting them to the lower-level objective.

[0123] Understandably, the step is to verify whether the upper-level optimization solution satisfies the frequency safety constraint, i.e., the inertia adequacy constraint, under perturbation conditions. If the constraint is not satisfied, an optimization cut is generated, and the process returns to the upper level to re-execute step S42.

[0124] Understandably, optimization cut is a special type of constraint. Based on the solutions to lower-level subproblems, it feeds back information about constraint non-compliance found in the lower-level checks to the upper-level master problem, guiding the master problem to adjust the unit combination scheme. It is usually expressed in the form of linear constraints. For example, based on the results of the lower-level checks, it determines which unit combinations or output conditions lead to insufficient inertia, and then generates corresponding constraints to prevent similar infeasible solutions from appearing in subsequent iterations of the upper-level master problem.

[0125] It should be noted that the optimized cut generated in this application can be an optimized cut that increases system inertia. While general optimized cuts are only used to eliminate infeasible solutions, the optimized cut in this application not only eliminates infeasible solutions but also actively guides the increase of system inertia. By identifying scenarios with insufficient inertia in the lower-level verification, the generated optimized cut requires the upper-level master problem to select a combination of generators that can increase system inertia. This optimized cut helps enhance the frequency stability of the power system, especially in scenarios with a high proportion of renewable energy integration.

[0126] Step S44: When the decision variables satisfy the lower-level objective, output the decision variables as the scheduling scheme.

[0127] Understandably, when the decision variables satisfy the lower-level objective, i.e., pass the inertia adequacy check, it means that the current upper-level optimal solution not only performs well in terms of power adequacy, capable of coping with uncertainties in load, wind power, and photovoltaic output, but also meets the system frequency security requirements in terms of inertia adequacy. This indicates that the unit combination scheme, after comprehensively considering multiple key factors such as economy, power balance, and frequency stability, is a feasible and high-quality dispatching scheme.

[0128] In this embodiment, by incorporating the load power change into the frequency response model, a quantitative characterization of the dynamic characteristics of the load-side inertia is achieved. This step overcomes the limitation of traditional models that rely solely on power supply parameters, establishing a complete analytical framework for the system frequency characteristics of the load-side dynamic response.

[0129] Next, based on the inertia adequacy constraint derived from the above model, the dynamic support capacity of the load side is transformed into mathematical constraints through the quasi-steady-state frequency deviation index. This step solves the key problem that load-side inertia cannot participate in scheduling due to the lack of dynamic representation, ensuring that the system inertia constraint simultaneously covers resources on both the source and load sides. Power adequacy constraints are obtained based on disturbance power and quasi-steady-state frequency deviation. The reduction in quasi-steady-state frequency deviation directly reflects the power deficit caused by the reduced load response, making the new power constraint implicitly include the equivalent reserve contribution of the load side, transforming the implicit reserve provided by load resources into an explicit power constraint. This step, through the coordinated optimization of load-side power support capacity and conventional power reserve, breaks through the limitation of traditional power constraints relying solely on unit reserves, reducing redundant reserve configurations.

[0130] Finally, by combining the aforementioned dual abundance constraints and system operation constraints for optimization, and considering that load-side inertia resources participate in the optimization scheduling in the form of dynamic constraints, a more economical scheduling scheme is generated under the premise of ensuring frequency security.

[0131] Furthermore, this application breaks through the limitations of traditional scheduling that only focuses on power adequacy and source-side inertia, innovatively introducing source and load inertia as optimization variables to construct a unit combination model that considers both inertia and power adequacy. The table below compares the traditional unit combination model, the unit combination model considering source-load stochasticity, and the model in this paper considering "inertia-frequency-power." The comparison shows that the model proposed in this invention, while considering source-load stochasticity, adds consideration of the source-load inertia-frequency contribution, incorporating the load-side frequency regulation contribution as effective frequency regulation power capacity into the total system reserve resources, thereby increasing the system's frequency regulation power capacity. On the other hand, the "frequency-inertia" related index constraints are converted into conditions to ensure that the system has a sufficient level of inertia.

[0132] .

[0133] Understandably, this application comprehensively utilizes the frequency regulation potential on both the source and load sides to achieve the coordinated optimization of the system's dynamic frequency response capability and reserve (power) capacity, which can effectively enhance the frequency stability and operational economy of the power system in scenarios with a high proportion of new energy sources.

[0134] To verify the effectiveness of this method, a modified IEEE 10-machine 39-bus system was used. Wind power, solar power output, and load forecasting data are as follows: Figure 8 As shown, further discussion follows. Specifically, the system reference frequency is set to 50Hz, the frequency change rate limit is set to -0.5Hz / s, the minimum frequency limit is set to 49Hz, and the allowable steady-state frequency deviation is set to 0.5Hz. Calculate the load frequency regulation effect coefficient. Confidence level. Take 0.96.

[0135] It should be noted that three schemes are set up and the scheduling results are analyzed: Scheme 1 is a conventional unit combination model that only considers power adequacy; Scheme 2 is a unit combination model that considers source-side inertia and power adequacy, but does not consider load frequency regulation; Scheme 3 is the unit combination model proposed in this application that considers both source and load, inertia and power adequacy.

[0136] It should be noted that, in order to clearly present the beneficial effects of this application, the following is combined with... Figures 9 to 13 The comparative analysis was conducted using the maximum frequency change rate, the lowest frequency point, and the reserve capacity as indicators.

[0137] It should be noted that, Figure 9These are inertial time constant diagrams for different time periods under different schemes provided in the embodiments of the method of this application. Figure 10 This is a graph showing the maximum frequency change rate over different time periods under different schemes provided in the embodiments of the method of this application. Figure 11 These represent the start-up and shutdown status of the generating units at different times under different schemes provided in the embodiments of the method of this application.

[0138] Scheme 1, which only considers power adequacy and neglects inertia adequacy, aims to balance disturbance power with reserve capacity. This results in a low number of units being started between 13 and 15, causing the system inertia level to fall below the minimum requirement. The maximum frequency change rate exceeds the safety threshold of -0.5Hz / s (reaching a maximum of -0.53Hz / s), threatening system frequency stability. In contrast, Scheme 2 and the scheme in this application (Scheme 3 in the figure) increase system inertia by adding more units, keeping the maximum frequency change rate within a safe range, fully demonstrating the necessity of considering inertia adequacy.

[0139] It should be noted that, Figure 12 These are the lowest frequency points in different time periods under different schemes provided in the embodiments of this application. Compared with Scheme 1, Scheme 2 increases the system inertia by adding operating units during the 13-15 time period to increase the system inertia by incorporating inertia adequacy constraints, thus raising the lowest frequency point during that time period; while this application further explores the potential of load frequency regulation effect, improving the system frequency response capability without increasing the number of operating units, raising the lowest frequency point in each time period by an average of about 6.13%, and enhancing the system frequency stability.

[0140] It should be noted that, Figure 13 This refers to the reserve capacity for different time periods under various schemes provided in the embodiments of this application. Since Scheme 1 and Scheme 2 follow the same reserve constraints, under the same power disturbance, both optimize the reserve capacity to the minimum required level with the goal of cost optimization, resulting in consistent reserve capacity performance across time periods. In contrast, this application, by taking into account the load frequency regulation effect and utilizing it to share part of the disturbance power, reduces the dependence on reserve capacity, alleviates the frequency regulation pressure on thermal power units, and reduces the average reserve capacity by 3.29% across time periods. The total reserve capacity is reduced by 3.31% compared to Scheme 2, improving system operating economy while reducing reserve costs.

[0141] The following describes the power system dispatching device that takes into account both inertia and power abundance provided in this application. The power system dispatching device that takes into account both inertia and power abundance described below can be referred to in correspondence with the power system dispatching method that takes into account both inertia and power abundance described above.

[0142] It should be noted that, as Figure 14As shown, the power system dispatching device considering both inertia and power adequacy includes: a model establishment module 10, used to determine the system frequency response model based on disturbance power, load power change, prime mover power change, and equivalent inertial time constant; a constraint acquisition module 20, used to acquire inertia adequacy constraints and quasi-steady-state frequency deviation based on the system frequency response model; and also used to acquire power adequacy constraints based on disturbance power and quasi-steady-state frequency deviation; and a dispatching output module 30, used to output a dispatching scheme based on inertia adequacy constraints, power adequacy constraints, and system operation constraints, according to the system objective function.

[0143] It is understood that the detailed functional implementation of each of the above units / modules can be found in the description of the aforementioned method embodiments, and will not be repeated here. The above device is used to execute the methods in the above embodiments. The implementation principle and technical effect of the corresponding program modules in the device are similar to those described in the above methods. The working process of the device can be referred to the corresponding process in the above methods, and will not be repeated here.

[0144] Based on the methods in the above embodiments, this application provides an electronic device, such as... Figure 15 As shown, the electronic device may include a processor 41, a communications interface 42, a memory 43, and a communication bus 44, wherein the processor 41, the communications interface 42, and the memory 43 communicate with each other via the communication bus 44. The processor 41 can call logical instructions in the memory 43 to execute the methods described in the above embodiments.

[0145] Furthermore, the logical instructions in the aforementioned memory 43 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application.

[0146] Based on the methods in the above embodiments, this application provides a computer-readable storage medium storing a computer program that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0147] Based on the methods in the above embodiments, this application provides a computer program product that, when run on a processor, causes the processor to execute the methods in the above embodiments.

[0148] It is understood that the processor in the embodiments of this application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. A general-purpose processor can be a microprocessor or any conventional processor.

[0149] The method steps in this application embodiment can be implemented in hardware or by a processor executing software instructions. The software instructions can consist of corresponding software modules, which can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, portable hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor, enabling the processor to read information from and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can reside in an ASIC.

[0150] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially as a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0151] It is understood that the various numerical designations used in the embodiments of this application are merely for the convenience of description and are not intended to limit the scope of the embodiments of this application.

[0152] Those skilled in the art will readily understand that the above description is merely a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A power system dispatching method considering both inertia and power adequacy, characterized in that, include: The system frequency response model is determined based on the disturbance power, load power change, prime mover power change, and equivalent inertial time constant. The inertia adequacy constraint and quasi-steady-state frequency deviation are obtained based on the system frequency response model. The inertia adequacy constraint includes: a maximum frequency change rate constraint, a minimum frequency point constraint, and a quasi-steady-state frequency deviation constraint. Obtaining the inertia adequacy constraint and quasi-steady-state frequency deviation based on the system frequency response model includes: obtaining the descent slope of the system frequency response model at the start of the disturbance as the maximum frequency change rate, and establishing a maximum frequency change rate constraint; obtaining the time when the descent slope of the system frequency response model is 0 as the time when the frequency reaches its minimum point, and obtaining the minimum frequency point based on the time when the frequency reaches its minimum point, and establishing a minimum frequency point constraint; obtaining the sustained frequency deviation after the system frequency response model enters the quasi-steady state as the quasi-steady-state frequency deviation, and establishing a quasi-steady-state frequency deviation constraint. Power adequacy constraints are obtained based on the disturbance power and the quasi-steady-state frequency deviation; wherein, the disturbance power includes: disturbance power of load, wind power output, and photovoltaic output; obtaining power adequacy constraints based on the disturbance power and the quasi-steady-state frequency deviation includes: obtaining the reserve capacity of the power system and the disturbance power of load, wind power output, and photovoltaic output; performing probabilistic quantization on the disturbance power to obtain disturbance power constraints; and obtaining power adequacy constraints based on the disturbance power, the reserve capacity, the quasi-steady-state frequency deviation, and the disturbance power constraints. Based on the inertia adequacy constraint, the power adequacy constraint, and the system operation constraint, a scheduling scheme is output according to the system objective function. This process includes: obtaining an upper-level objective considering power adequacy for unit combination optimization and a lower-level objective considering inertia adequacy verification based on the system objective function; solving the upper-level objective to obtain the system's decision variables and inputting them into the lower-level objective; generating an optimization cut and inputting it into the upper-level objective when the decision variables do not satisfy the lower-level objective; and executing the step of solving the upper-level objective to obtain the system's decision variables and inputting them into the lower-level objective. The optimization cut includes constraints on decision variables that do not satisfy the lower-level objective; and outputting the decision variables as the scheduling scheme when the decision variables satisfy the lower-level objective.

2. The power system dispatching method considering both inertia and power adequacy as described in claim 1, characterized in that, The system frequency response model is as follows: ； ； ； ； ； ； in, This refers to the system frequency deviation. For time; The disturbance power; This refers to the unit's droop coefficient; The equivalent inertial time constant of the unit; The power coefficient of the high-pressure cylinder of the prime mover; This is the load frequency regulation effect coefficient; The mechanical power factor; This is the unit's reheat time constant; For the natural oscillation angular frequency, For damping ratio, For damped oscillation angular frequency, and These are intermediate variables in the calculation process.

3. The power system dispatching method considering both inertia and power adequacy as described in claim 1, characterized in that, The disturbance power is probabilistically quantized to obtain disturbance power constraints, including: Obtain the prediction error variables for power system load, wind power output, and photovoltaic power output; The disturbance power is constrained based on the prediction error variable and the preset confidence level, and the probability distribution model of the prediction error is obtained as the constraint on the disturbance power.

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