CN117318027A - EMD-based real-time day-ahead frequency regulation and control method for virtual power plant - Google Patents
EMD-based real-time day-ahead frequency regulation and control method for virtual power plant Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The invention discloses an EMD-based real-time day-ahead frequency regulation method for a virtual power plant, which comprises the following steps of: decomposing the frequency modulation required power based on an empirical mode decomposition method, distributing adjustable resources of the virtual power plant to each mode component according to a decomposition result, and classifying various resources stored by source load in the virtual power plant according to high, medium and low frequency components; establishing a day-ahead scheduling objective function; taking the resources of each part of the source load storage of the virtual power plant into consideration, providing related constraint condition expressions of each part of the source load storage, and establishing daily scheduling optimization of the virtual power plant; simplifying according to a power-frequency transfer function, and establishing a virtual power plant aggregation frequency response model; according to the virtual power plant aggregation frequency response model, a power-frequency domain expression is obtained, and a frequency time-varying expression is obtained through inverse Laplace transformation; extracting a frequency quality evaluation index according to the frequency time-varying expression; and carrying out real-time optimized scheduling by taking the frequency quality index as an objective function, and distributing each unit of frequency modulation capacity task.
Description
Technical Field
The invention relates to an EMD-based real-time day-ahead frequency regulation and control method for a virtual power plant, and belongs to the technical field of power grid dispatching control.
Background
In recent years, the power load is rapidly increased, the speed increasing speed is obviously higher than the electric quantity speed increasing speed, the peak-valley difference is continuously increased, and the difficulty and contradiction of system peak regulation are obvious. On the other hand, with the proposal of a double-carbon target, the new energy is developed rapidly, the problem of the digestion is outstanding, and the higher requirement on the regulating capacity of the power system is provided. However, considering the direct current landing from the middle to the large scale, the market share of the traditional unit is extruded, and the controllable resources on the supply side are reduced.
The demand side has huge load regulation and control potential, and along with the rapid increase of industrial productivity, the improvement of living standard of people and the growth of third industry in the late epidemic situation, the capacity of various loads such as industrial load, commercial load, resident load and the like is rapidly improved, and the wind-solar-energy and other distributed new energy sources are accessed in a large scale. In addition, the flexible resource of source storage such as distributed wind power, photovoltaic, energy storage and the like has considerable regulation and control potential and needs to be excavated. The polymorphic flexible resources can provide various auxiliary services for the power system and the safe operation support of the power grid, promote the consumption of new energy, and can be aggregated into a virtual power plant form to improve the operation characteristics of the power grid. A set of reasonable virtual power plant regulation and control strategies are designed to effectively and orderly call large-scale flexible resources, so that the problem to be solved is urgent at present.
Disclosure of Invention
The invention aims to provide an EMD-based virtual power plant day-ahead real-time frequency regulation method for carrying out EMD decomposition on a current frequency curve, decomposing an AGC frequency modulation task and providing a virtual power plant day-ahead real-time scheduling method, and aims to provide an efficient and accurate virtual power plant frequency modulation control method.
In order to achieve the above purpose, the present invention provides the following technical solutions: an EMD-based real-time day-ahead frequency regulation method for a virtual power plant comprises the following steps:
s1, decomposing frequency modulation required power based on an empirical mode decomposition method, and distributing adjustable resources of a virtual power plant for each mode component according to a decomposition result;
s101, adopting an empirical mode decomposition method, and decomposing deviation data into a high-frequency component, a medium-frequency component and a low-frequency component 3 part by selecting a proper filtering order;
s102, for the high-frequency component, new energy sources such as wind power, photovoltaic and the like are controlled by adopting AGC to regulate and control the high-frequency component;
s103, for the intermediate frequency component, regulating and controlling the intermediate frequency component by adopting an AGC control energy storage device;
s104, for the low-frequency components, flexible loads such as sagging control electric vehicles, air conditioners and the like are adopted to regulate and control the low-frequency components;
s2, classifying various resources of source load storage in the virtual power plant according to high, medium and low frequency components, scheduling the virtual power plant in the future on the basis of the classification, and reserving the maximum adjustable capacity for the frequency modulation of the participation system of the virtual power plant;
s201, classifying various resources stored by source load in a virtual power plant according to high, medium and low frequency components;
s202, a day-ahead scheduling objective function is established by considering the cost problem, the new energy consumption problem, the peak-valley difference problem and the frequency modulation standby capacity problem of the virtual power plant, and the objective function is divided into three types: the total dispatching cost is minimum, the capacity of the virtual power plant for eliminating new energy is strongest, and the capacity of the virtual power plant for peak clipping and valley filling is strongest;
s203, taking the resources of each part of the source load storage of the virtual power plant into consideration, providing related constraint condition expressions of each part of the source load storage, and establishing daily scheduling optimization of the virtual power plant;
s3, establishing a virtual power plant aggregation frequency response model, establishing a frequency time-varying expression according to the virtual power plant aggregation frequency response model, extracting a frequency quality evaluation index, performing virtual power plant real-time scheduling optimization, and distributing each unit frequency modulation capacity task;
s301, simplifying according to a power-frequency transfer function, and establishing a virtual power plant aggregation frequency response model;
s302, obtaining a power-frequency domain expression according to a virtual power plant aggregation frequency response model, and obtaining a frequency time-varying expression through inverse Laplace transformation;
s303, extracting a frequency quality evaluation index according to the frequency time-varying expression;
and S304, carrying out real-time optimized scheduling by taking the frequency quality index as an objective function, and distributing each unit of frequency modulation capacity task.
Compared with the prior art, the invention has the beneficial effects that: the frequency components obtained based on the EMD decomposition method are further regulated and controlled, and better frequency regulation and control are achieved through a day-ahead real-time scheduling method.
Drawings
FIG. 1 is a schematic flow chart of the EMD-based virtual power plant day-ahead-real-time frequency regulation method of the invention;
FIG. 2 is a simplified control block diagram of a multi-virtual synchronous machine aggregate grid tie.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. The specific embodiments described herein are only for the purpose of illustrating the technical solution of the present invention and are not to be construed as limiting the invention.
As shown in fig. 1, an EMD-based real-time frequency regulation method for a virtual power plant includes the following steps:
s1: and decomposing the frequency modulation required power based on an empirical mode decomposition method, and distributing adjustable resources of the virtual power plant for each mode component according to a decomposition result.
S101: adopting an empirical mode decomposition method, and decomposing deviation data into a high-frequency power part, a medium-frequency power part and a low-frequency power part by selecting a proper filtering order;
empirical Mode Decomposition (EMD) is the decomposition of a signal into a plurality of eigenmode functions (IMFs) based on the local characteristics of the time scale of the signal sequence. The method can stabilize the non-stationary signal by adopting the EMD method, and a time-frequency spectrogram is obtained by utilizing the Hilbert variation method, so that compared with the traditional Fourier transform decomposition method and wavelet transform decomposition method, the EMD method is more visual. The invention adopts the EMD method to decompose the frequency modulation power requirement into high frequency, medium frequency and low frequency parts, and the high frequency, medium frequency and low frequency parts are used as the reference output power of flexible resources such as source load storage and the like.
Constructing an EMD space-time filter, and decomposing the thermal power unit frequency modulation deviation data to obtain a plurality of IMF componentsQuantity L i (i=1, 2,) m. The results after decomposition by the EMD method are as follows:
wherein: e (t) is thermal power unit frequency modulation deviation data; l (L) i (t) represents the decomposed ith frequency-modulated component; l (L) R (t) represents a decomposition remainder; l (L) l 、l h The number of IMF components of high, middle and low frequency components set for EMD decomposition symbolizes the frequency demarcation points of the high, middle and low frequency components; ΔP h 、ΔP g 、ΔP l Is the high, medium and low frequency components obtained by decomposition through EMD method.
S102: for high-frequency power, AGC is adopted to control new energy sources such as wind power, photovoltaic and the like to regulate and control high-frequency components;
because the response of the load side flexible resource to the power change is relatively slow, the change frequency is small, the service life of the energy storage device is greatly reduced by high-frequency scheduling, and new energy sources such as wind and light with high-frequency change output are the best choice for adjusting high-frequency power. Due to the fact that new energy sources such as wind and light are frequently used for adjustment, frequent climbing compensation is required to be given to source side resources:
S SP ＝a|P new (t+T i )-P new (t)|T i (3)
where a is the climbing cost per MW, P new Is the output of new energy, T i Is the climbing time.
S103: for the intermediate frequency power, an AGC (automatic gain control) energy storage device is adopted to regulate and control an intermediate frequency component;
because the load side flexible resource has relatively slow response to power change, small change frequency and slow response speed, the AGC is adopted to control the energy storage device to regulate and control the medium frequency power. For energy storage devices, frequent charging and discharging also accelerates its loss, thus giving corresponding charge and discharge compensation:
wherein:the energy storage participates in AGC discharge electric quantity for the period i; />And storing the charged electric quantity participating in AGC frequency modulation for the i period. />And respectively charging and discharging the energy storage in the period i to participate in the compensation electricity price of the AGC.
S104: for low-frequency power, flexible loads such as sagging control electric vehicles and air conditioners are adopted to regulate and control low-frequency components, and the overall regulation and control adopts a parallel control mode, so that the regulation and control of high, medium and low frequency parts are simultaneously carried out;
because the low-frequency power part has lower requirement on the response speed of flexible resource adjustment, the droop control method is adopted to control the load-side flexible resource to adjust the low-frequency power. The sagging control method is generally as follows:
in the invention, only a frequency modulation part is considered, and m is a frequency sagging coefficient; n is the voltage sag coefficient; f (f) 0 、U 0 The working frequency and the voltage are the original working frequency and the original working voltage; f (f) * 、U * The working frequency and the voltage are adjusted; p, Q is the active and reactive power involved in the regulation. The invention adopts the difference adjustment coefficient of various loads to replace the sagging coefficient of frequency, and the adjustment quantity distribution formula is as follows:
ΔP Ti is the adjusted active power variation allocated by the ith scheduling element over the scheduling period T.
The traditional parallel control mode depends on a plurality of established control modes and threshold values to control each resource of the virtual power plant to execute the scheduling command simultaneously in different proportions, and the task allocation and control modes are too simple and dead to achieve an optimal result although the execution speed of the virtual power plant instruction is greatly compressed.
S2: the method comprises the steps of classifying various resources stored by source load in the virtual power plant according to high, medium and low frequency components, scheduling the virtual power plant in the future on the basis of the classification, and reserving the maximum adjustable capacity for the frequency modulation of the participation system of the virtual power plant.
S201: and classifying various resources stored by the source load in the virtual power plant according to the high, medium and low frequency components.
Classifying the virtual power plant resources according to the distribution of the high, medium and low frequency components in the step S1;
s202: and (3) establishing a daily scheduling objective function by considering the cost problem, the new energy consumption problem, the peak-valley difference problem and the frequency modulation standby capacity problem of the virtual power plant.
Considering the maximum functioning of a virtual power plant, the objective functions are divided into three classes: the total dispatching cost is minimum, the capacity of the virtual power plant to eliminate new energy is strongest, and the capacity of the virtual power plant to cut peaks and fill valleys is strongest.
(1) Total cost of day-ahead schedule
The aim of minimizing annual VPP energy storage investment and primary frequency modulation cost is to include wind power energy storage investment cost F life Wind power energy storage loss cost F loss Standby cost F for wind power energy storage W User load demand response cost F buy Cost of frequency modulation F f Punishment cost F of insufficient frequency modulation of system pun As shown in formulas (8), (12), (13), (11), (15).
f co ＝min{F life +F buy +F loss +F W +F pun } (7)
1) Wind power energy storage investment cost. The investment cost can be mainly divided into construction cost F 1 And operation maintenance cost F 2 Two parts, namely:
F life ＝F 1 +F 2 (8)
wherein: r is the energy storage depreciation rate; n (N) set The service life is set for energy storage construction; alpha P 、α E The energy storage power and the capacity unit price are respectively; alpha fP 、α fE Annual operation and maintenance costs of energy storage unit power and capacity respectively; p (P) Bat 、E Bat Respectively the rated power and the capacity of the energy storage.
2) VPP responds to the cost of purchasing power. When the VPP is downwards modulated, power is absorbed from the system; when the frequency is modulated upwards, power is released to the system, and the electricity purchasing cost in the process of the electricity grid charging and discharging transaction is as follows:
wherein: alpha buy 、α sell The price of electricity purchasing and selling is respectively; k is the frequency modulation frequency; p (P) B,up (i)、P B,down (i) Energy is stored to upwards and downwards modulate the frequency power when the ith frequency modulation is performed; t (T) f For the primary frequency modulation duration.
3) Energy storage loss cost. In the daily working process of wind power and energy storage, due to the problems of self operation efficiency and self power regulation, energy loss is inevitably generated in the operation process, and the energy storage charge and discharge loss cost F loss Can be expressed as:
wherein: η (eta) c 、η d And the charging and discharging efficiencies of the energy storage device are respectively.
4) Wind power standby cost. In order to cope with sudden power fluctuation, a wind power plant generally has a certain reserve capacity which is also added into the cost, and the reserve cost F W Can be expressed as:
wherein: t (T) s1 The wind speed sampling period is; n is the sampling times of annual wind speed; alpha W The wind power unit price is used for surfing the net; p (P) max (i) And (5) the theoretical maximum output of the wind power plant at the ith sampling.
5) Insufficient frequency modulation penalizes costs. According to national standards, the primary frequency modulation dead zone of the power system is generally (+/-) (0.03-0.1) Hz, and when the output of the wind power plant is greater than 20% of the rated output, the wind power plant should participate in system frequency adjustment. Wind farm frequency modulation power magnitude ΔP p (i) Should satisfy
Wherein P is N (i) Rated power of the ith wind turbine generator; Δf is the amount of power change; f (f) B Is the reference frequency; k (K) P And the difference adjustment coefficient is the difference adjustment coefficient of the wind turbine generator.
When the VPP comprehensive frequency modulation power cannot meet the primary frequency modulation requirement of the power system, the power system operator needs to punish the insufficient frequency modulation of the VPP, and the cost F pun The method comprises the following steps:
wherein: alpha pun Punishment unit price for insufficient frequency modulation of the system; p (P) need (i) For adjustingFrequency power demand.
(2) New energy capacity of virtual power plant
In order to consume new energy output, namely to minimize the wind power output waste wind quantity, the method can be expressed by the following objective function.
Wherein: p (P) W,max The theoretical maximum output of the fan; p (P) W,i The actual output of the fan is obtained.
On the premise of ensuring that the virtual power plant maximally dissipates new energy, the day-ahead scheduling also needs to provide enough spare capacity for power system frequency adjustment, namely:
wherein:the response potential, i.e., reserve capacity, of each portion is stored for the virtual power plant source load.
(3) Peak clipping and valley filling
One of the main purposes of the power supply party to execute virtual power plant scheduling is to cut peaks and fill valleys, so that the load curve tends to be flat. Even a reaction to frequency regulation is made if the system load peak-to-valley difference is enlarged in order to regulate the power system frequency quality. Therefore, in the invention, a load deviation function is established to evaluate the benefit brought by the demand response, and the benefit obtained by the power supply party in the model can be represented by the load deviation function:
wherein L is a load curve of the power system; l (L) t Load amount for t period; t is the number of time periods scheduled before the day.
Thus, the objective function of the day-ahead schedule can be obtained as:
F da ＝f co +f ab +f res +f L (19)
s203: and (3) taking resources of each part of the source load storage of the virtual power plant into consideration, providing related constraint condition expressions of each part of the source load storage, and establishing daily scheduling optimization of the virtual power plant.
For the objective function F da Mainly contains the following constraints.
1) And the frequency modulation capacity of the wind storage system is restricted.
According to the opportunity constraint planning theory, because wind power and energy storage have randomness in output, complete control cannot be achieved on the wind power output condition, and therefore the probability of requiring each unit of virtual power plant source charge storage to meet the frequency modulation power requirement is required to be larger than a certain confidence degree beta 1 The method comprises the following steps:
wherein: p (P) r {. The probability characterization of the form in brackets; ΔP p Adjustable capacity, MW, available for virtual power plant units; p (P) need The adjustable capacity, MW, required for the secondary frequency modulated virtual power plant unit.
For the ith frequency modulation, the VPP unit state variable m (i) is set:
then simultaneous availability, the probability that VPP meets the primary power demand of the power system is:
2) Energy storage and wind power climbing constraint
Wherein: p (P) B,up (i)、P B,down (i) The climbing speed is MW/s of the energy storage charging and discharging power; p (P) up,max (i)、P down,max (i) The maximum value of the climbing rate of the energy storage charging and discharging power is MW/s.
3) And (5) energy storage charging and discharging state constraint.
Wherein: u (u) up (i)、u down (i) The charge and discharge flags for the energy storage device are typically set to 0-1 variables. The constraint defines that the stored energy is not allowed to be charged and discharged simultaneously.
4) And (5) energy storage SOC constraint.
Wherein S is 0 Is the initial value of the energy storage SOC; s is S min Is the lower limit of the energy storage SOC; s is S max Is the upper limit of the stored energy SOC. The constraint defines that the stored energy SOC remain within a certain safe range.
S3: and establishing a virtual power plant aggregation frequency response model, establishing a frequency time-varying expression according to the virtual power plant aggregation frequency response model, extracting frequency quality evaluation indexes, performing virtual power plant real-time scheduling optimization, and distributing each unit of frequency modulation capacity task.
S301: and simplifying according to the power-frequency transfer function, and establishing a virtual power plant aggregation frequency response model.
The virtual power plant and the conventional thermal power generating unit are subjected to parallel aggregation treatment, and a dynamic frequency response equivalent model of the multi-machine VPP system can be obtained:
in FIG. 2, J W,sys 、D W,sys 、K p,W The virtual inertia, the damping coefficient and the sagging control coefficient are respectively aggregated for the wind turbine generator; j (J) SOC,sys 、D SOC,sys 、K p,SOC Virtual inertial of energy storage devices respectivelyAmount, damping coefficient, and droop control coefficient. J (J) L,sys 、D L,sys 、K p,L The virtual inertia, damping coefficient and sag control coefficient of the flexible load are respectively. K (K) p,F Is the sagging control coefficient of the thermal power generating unit, F HP T is the proportion of high-pressure cylinders of steam turbines R The inertia time constant of the turbine is obtained by using the three parameters to assemble inertia links of the heat-engine plant turbine participation system with the frequency modulation. K (K) p,F Is a sagging control coefficient of the thermal power generating unit; f (F) HP The high-pressure cylinder ratio of the steam turbine; t (T) R Is a turbine inertia time constant; m is M F 、D F The equivalent inertia and the equivalent damping coefficient of the conventional thermal power generating unit are respectively; s is the Laplace operator; omega 0 Is the system working power.
S302: and obtaining a power-frequency domain expression according to the virtual power plant aggregation frequency response model, and obtaining a frequency time-varying expression through inverse Laplace transformation.
From the frequency response aggregation model obtained in fig. 2, the transfer function expression of the system can be obtained as follows:
when the system has power shortage, the instantaneous change of the system power can be regarded as the step response, i.e. L [ delta ] P e (t)]＝ΔP e (s)/s, so that the equation and the above equation are combined and then inverse Laplace transformation is performed, and a time domain expression of a system frequency response equation can be obtained:
s303: and extracting a frequency quality evaluation index according to the frequency time-varying expression.
For the frequency quality in the primary frequency modulation process, three indexes are introduced in the invention to evaluate the frequency quality: maximum rate of frequency change rocofs ini Minimum point frequency deviation Δf nadir Steady state frequency deviation Δf ∞ 。
1) Rate of change of frequency
The rate of frequency change in an electrical power system is the most relevant technical indicator to the inertia of the system. When the system is disturbed, the power grid is subjected to power impact, the inertia response acts immediately, the generator speed regulator does not act yet, and the frequency change rate rocofis completely dependent on the inertia of the system. And when system 0 + At the moment, the system receives a power step response, at the moment, the system frequency is reduced most, the frequency change rate is the largest, and the function expression of the maximum frequency change rate can be obtained as follows:
2) Minimum frequency deviation
The lowest point frequency is the frequency of the frequency after the power shortage of the system, at the moment when the system stops descending for the first time and starts to rise back in the recovery process, and the index determines whether the system frequency can be recovered to a stable state or not. When the frequency reaches the lowest point, there areIt can be derived that the frequency deviation at which the nadir frequency occurs is:
3) Steady state frequency error
It is considered that at t→infinity, the system frequency reaches a quasi-steady state. In the frequency domain system, when t→infinity, s→0, the steady-state frequency error of the system can be obtained by the final value theorem, as shown in the formula (34).
S304: and carrying out real-time optimized scheduling by taking the frequency quality index as an objective function, and distributing each unit of frequency modulation capacity task.
The objective function of real-time scheduling can take three types of frequency indexes as main bodies, namely:
F short ＝min{f RoCoF +f nadir +f ∞ } (35)
wherein: f (F) RoCoF To refer to an objective function of maximum frequency change rate; f (F) nadir An objective function representing the minimum frequency deviation; f (F) ∞ Is an objective function that refers to steady state frequency deviation.
1) Power balance constraint
Wherein: subscripts W, SOC, L, H respectively refer to wind turbines, energy storage devices, loads and conventional thermal power plants in the power system. The meaning of each variable in the above equation is described above, and will not be described in detail herein.
2) Wind curtailment constraint
S W，max,i,t -P W,i,t ＜A W,max,i,t
Wherein: s is S W，max,i,t The maximum output of the ith wind turbine generator set at the time t is obtained; a is that W,max,i,t The maximum air discarding quantity can be expressed by proportion; ρ W,i,t Is the wind power minimum output power ratio. The two restrict the lowest value of wind power output, and help the VPP to consume wind power.
3) Standby demand constraints
Wherein: beta W,i,t 、β SOC,i,t 、β H,i,t The standby demand proportion of the wind turbine generator, the energy storage equipment and the conventional thermal power generating unit is respectively indicated; during the scheduling process, it must be satisfied that it has enough spare capacity to face various emergency situations of the burst.
The foregoing description of the preferred embodiments of the present invention has been presented only in terms of those specific and detailed descriptions, and is not, therefore, to be construed as limiting the scope of the invention. It should be noted that modifications, improvements and substitutions can be made by those skilled in the art without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (4)
1. The day-ahead-real-time frequency regulation and control method of the virtual power plant based on the EMD is characterized by comprising the following steps of:
s1, decomposing frequency modulation required power based on an empirical mode decomposition method, and distributing adjustable resources of a virtual power plant for each mode component according to a decomposition result;
s2, classifying various resources of source load storage in the virtual power plant according to high, medium and low frequency components, scheduling the virtual power plant in the future on the basis of the classification, and reserving the maximum adjustable capacity for the frequency modulation of the participation system of the virtual power plant;
and S3, establishing a virtual power plant aggregation frequency response model, establishing a frequency time-varying expression according to the virtual power plant aggregation frequency response model, extracting a frequency quality evaluation index, performing virtual power plant real-time scheduling optimization, and distributing each unit frequency modulation capacity task.
2. The EMD-based virtual power plant day-ahead-real-time frequency regulation method according to claim 1, wherein the step S1 comprises the steps of:
s101, decomposing deviation data into a high-frequency component, a medium-frequency component and a low-frequency component 3 by adopting an empirical mode decomposition method;
s102, for the high-frequency component, regulating and controlling the high-frequency component by adopting AGC to control the distributed new energy;
s103, for the intermediate frequency component, regulating and controlling the intermediate frequency component by adopting an AGC control energy storage device;
s104, for the low-frequency component, controlling the low-frequency component by adopting a droop control flexible load.
3. The EMD-based virtual power plant day-ahead-real-time frequency regulation method according to claim 1, wherein the step S2 comprises the steps of:
s201, classifying various resources stored by source load in a virtual power plant according to high, medium and low frequency components;
s202, a day-ahead scheduling objective function is established, and the objective function is divided into three types: the total dispatching cost is minimum, the capacity of the virtual power plant for eliminating new energy is strongest, and the capacity of the virtual power plant for peak clipping and valley filling is strongest;
and S203, providing related constraint condition expressions of each part of the source load storage, and establishing daily scheduling optimization of the virtual power plant.
4. The EMD-based virtual power plant day-ahead-real-time frequency regulation method according to claim 1, wherein the step S3 comprises the steps of:
s301, simplifying according to a power-frequency transfer function, and establishing a virtual power plant aggregation frequency response model;
s302, obtaining a power-frequency domain expression according to a virtual power plant aggregation frequency response model, and obtaining a frequency time-varying expression through inverse Laplace transformation;
s303, extracting a frequency quality evaluation index according to the frequency time-varying expression;
and S304, carrying out real-time optimized scheduling by taking the frequency quality index as an objective function, and distributing each unit of frequency modulation capacity task.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117674197A (en) * | 2024-01-31 | 2024-03-08 | 南京邮电大学 | Frequency adjustment method, storage medium and equipment using virtual power plant active support |
| CN118094965A (en) * | 2024-04-26 | 2024-05-28 | 国网浙江省电力有限公司经济技术研究院 | Virtual power plant frequency response aggregation equivalence method, device and storage medium |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117674197A (en) * | 2024-01-31 | 2024-03-08 | 南京邮电大学 | Frequency adjustment method, storage medium and equipment using virtual power plant active support |
| CN117674197B (en) * | 2024-01-31 | 2024-04-16 | 南京邮电大学 | Frequency adjustment method, storage medium and equipment using virtual power plant active support |
| CN118094965A (en) * | 2024-04-26 | 2024-05-28 | 国网浙江省电力有限公司经济技术研究院 | Virtual power plant frequency response aggregation equivalence method, device and storage medium |
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