CN111756039B - New energy power system inertia estimation method based on probability statistics - Google Patents

New energy power system inertia estimation method based on probability statistics Download PDF

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CN111756039B
CN111756039B CN202010658194.XA CN202010658194A CN111756039B CN 111756039 B CN111756039 B CN 111756039B CN 202010658194 A CN202010658194 A CN 202010658194A CN 111756039 B CN111756039 B CN 111756039B
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new energy
energy power
power system
inertia
frequency
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CN111756039A (en
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杨松浩
赵一铭
郝治国
李子昂
张保会
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Xian Jiaotong University
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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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • 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
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • 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
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • 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
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/28—The renewable source being wind energy
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/76—Power conversion electric or electronic aspects

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  • Engineering & Computer Science (AREA)
  • Power Engineering (AREA)
  • Wind Motors (AREA)
  • Control Of Eletrric Generators (AREA)

Abstract

The invention discloses a new energy power system inertia estimation method based on probability statistics, which comprises the steps of setting a frequency starting threshold value, a sliding time window length and a calculation termination time, and recording the time when the system frequency reaches the threshold value as t1And let t2＝t1+ Δ T; separately obtain t1And t2The active output of each wind power plant, the active demand of each load and the system frequency change rate are obtained at all times; expanding a synchronous machine rotor motion equation into a fan output form, and performing difference to obtain an inertia observed value M of the new energy power systemi(ii) a And continuously sliding the time window until the termination moment, and fitting a probability distribution curve according to the distribution rule of the system inertia observation value to obtain an accurate estimation value of the system inertia. According to the method, only equivalent measurement information of fan output, load demand and system frequency response is needed, the system inertia can be accurately estimated under the influence of adverse factors such as fan output randomness, measurement data noise and the like according to the probability statistics principle, and the method has important significance for frequency stability analysis and control of the new energy power system.

Description

New energy power system inertia estimation method based on probability statistics
Technical Field
The invention belongs to the technical field of new energy power systems, and particularly relates to a new energy power system inertia estimation method based on probability statistics.
Background
Under the large background of the new-generation energy revolution and energy transformation, the high-proportion new energy access becomes the main development trend of the new-generation power system in China. Taking wind power as an example, the installed capacity and installed proportion thereof are continuously increasing in recent years. However, while providing clean electric energy and reducing carbon emission, the new energy also brings great challenges to the safe and stable operation of the power system, which is mainly reflected in that the new energy generator replaces a large number of synchronous generators, but because the new energy generator does not have rotational inertia, the inertia level of the system is significantly reduced, and thus the frequency stability of the system is deteriorated. In order to solve this problem, a great deal of research has been conducted on a method for analyzing and controlling the frequency stability of a system from the viewpoint of the system or the power generation of new energy. The method is used for solving the problem that how to accurately obtain the inertia of a new energy power system is an extremely critical and important problem.
However, the inertia of the new energy power system is difficult to estimate, and the reason is mainly that, firstly, the inertia characteristics of the system are obviously changed due to the fact that new energy power generation is connected to a power grid through a power electronic converter, so that the inertia of the new energy power system cannot be estimated according to the traditional method. Secondly, considering factors such as the randomness of fan output and the noise of measured data, if the system inertia is estimated only through the measured information at a certain moment, larger calculation noise and observation error are caused. Therefore, accurately estimating the inertia of the new energy power system must solve the above-described problem.
Disclosure of Invention
In order to solve the problem that the system inertia is difficult to estimate due to high-proportion access of new energy such as wind power and the like, the invention aims to provide a new energy power system inertia estimation method based on probability statistics. According to the method, the inertia observed value of the system is calculated in real time only by measuring information such as fan output, load demand and system frequency response, and the accurate estimated value of the inertia of the new energy power system is obtained under the influence of adverse factors such as fan random output and measured data noise according to the probability statistics principle.
In order to achieve the purpose, the invention adopts the following technical scheme:
the new energy power system inertia estimation method based on probability statistics comprises the following steps:
step 1: setting a frequency start threshold fshThe sliding time window length DeltaT and the calculation termination time Tend；
Step 2: judging whether the current new energy power system frequency reaches a frequency starting threshold value fshIf the frequency reaches the frequency starting threshold value, the moment when the frequency of the new energy power system reaches the frequency starting threshold value is recorded as t1And let t2＝t1+ Δ T; otherwise, repeating the step 2;
and step 3: respectively obtaining t through real-time measurement1And t2Active power output P of all wind power plants of new energy power system at two momentsWiActive demand P of each loadLiAnd the frequency change rate of the new energy power system
Figure BDA0002577530700000021
And 4, step 4: expanding the rotor motion equation of the synchronous machine into a form containing fan output, and applying t1,t2The rotor motion equations at two moments are subjected to difference making to obtain an inertia observed value M of the new energy power systemi；
And 5: the sliding time window is continued for a length deltaT until the end time TendObserving the inertia of the new energy power system obtained in the whole transient process;
step 6: counting inertia observed values of the new energy power system obtained through calculation in the transient process, and drawing an inertia observed value distribution diagram of the new energy power system;
and 7: obtaining an accurate estimated value M of the inertia of the new energy power system by fitting a probability distribution curve according to the distribution rule of the inertia observed values of the new energy power systemeq。
The expanded synchronous machine rotor motion equation containing the fan output form is shown as the formula (1):
Figure BDA0002577530700000031
in the formula: m is the sum of inertia of all synchronous units of the new energy power system; pWThe sum of the active power output of all fans in the new energy power system; pmFor all in the new energy power systemSum of mechanical power of synchronous machines; pLThe sum of the active demands of all loads in the new energy power system; pLossThe network loss of the new energy power system; f. ofcoiThe frequency of the inertia center of the new energy power system.
Inertia observed value M of new energy power systemiThe calculation formula (2) is shown as follows:
Figure BDA0002577530700000032
in the formula: miIs the observed inertia value of the system.
Preferably, the frequency start threshold fshThe value of (1) is calculated by referring to the action threshold value of the first low-frequency load shedding wheel, the value range of the sliding time window length delta T is 300-500ms, and the termination time T is calculatedendThe value range of (A) is 30-50 s.
The method of the invention needs to preset a frequency starting threshold value fshThe sliding time window length DeltaT and the calculation termination time TendAnd the time when the system frequency f reaches the starting threshold value is recorded as t1Then, set t2＝t1+ Δ T; secondly, respectively obtaining t through a real-time measurement technology1And t2Active power output P of all wind power plants at two momentsWiActive demand P of each loadLiAnd rate of change of system frequency
Figure BDA0002577530700000033
Then, expanding the rotor motion equation of the synchronous machine into a form containing fan output, and carrying out t1,t2The rotor motion equations at two moments are subjected to difference making to obtain an inertia observed value M of the new energy power systemi(ii) a Thereafter, the sliding time window Δ T is continued until the end time TendObserving inertia in the whole transient process after the system is disturbed; and finally, according to the distribution rule of the system inertia observation data, obtaining an accurate estimated value of the system inertia by fitting a probability distribution curve. The method only needs the measurement information of fan output, load demand, system frequency response and the likeAccording to the probability statistics principle, the influence of adverse factors such as calculation errors, calculation noise and random fan output can be effectively reduced, the system inertia can be accurately estimated, and the method has important significance for frequency stability analysis and control of the new energy power system.
Drawings
FIG. 1 is a flow chart of a method of implementing the present invention.
Fig. 2 is a topology diagram of an IEEE-39 node system.
FIG. 3 is a wind speed curve simulating a random noise wind.
FIG. 4 is an inertia observation M for the systemiDistribution diagram and normal fitting curve.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples.
As shown in fig. 1, the method for estimating inertia of a new energy power system based on probability statistics of the present invention includes the following steps:
step 1: setting a frequency start threshold fshThe sliding time window length DeltaT and the calculation termination time Tend(ii) a Wherein f isshThe value can refer to the action threshold value of the first low-frequency load shedding wheel, the value range of delta T is 300-500ms, and T isendThe value range of (1) is 30-50 s;
step 2: judging whether the current new energy power system frequency reaches a frequency starting threshold value fshIf the frequency reaches the frequency starting threshold value, recording the moment when the frequency of the new energy power system reaches the frequency starting threshold value as t1And is given t2Is t1+ Δ T; otherwise, repeating the step 2 to continue judging;
and step 3: respectively obtaining t through real-time measurement1And t2Active power output P of all wind power plants of new energy power system at two momentsWiActive demand P of each loadLiAnd the frequency change rate of the new energy power system
Figure BDA0002577530700000041
And 4, step 4: expanding the equation of motion of a rotor of a synchronous machineIs developed into a form containing fan output
Figure BDA0002577530700000051
And will t1Equation of motion of rotor at time
Figure BDA0002577530700000052
And t2Equation of motion of rotor at time
Figure BDA0002577530700000053
Performing difference making to obtain an inertia observed value of the new energy power system
Figure BDA0002577530700000054
And 5: the sliding time window is continued for a length deltaT until the end time TendInertia value M of new energy power system obtained in the whole transient processiCarrying out observation;
step 6: counting inertia observed values of the new energy power system obtained through calculation in the transient process, and drawing an inertia observed value distribution diagram of the new energy power system;
and 7: obtaining an accurate estimated value M of the inertia of the new energy power system by fitting a probability distribution curve according to the distribution rule of the inertia observed value of the new energy power systemeq。
Example (b):
the feasibility of the scheme is illustrated by taking an IEEE-39 node system under high wind power permeability as an example. As shown in fig. 2, the system fault is set to be that the lines 1-39, 3-4, 16-17 are disconnected at the moment when t is 0.5s, and the system forms an upper half plane isolated network; the generator G-37 is set as a double-fed wind generator which cannot provide inertia support for the system; the wind speed is set to random wind, as shown in FIG. 3; the rest units are conventional steam turbine units, the wind power permeability of the system is 33.33%, and the specific parameters are shown in table 1:
TABLE 1 inertia of generators in the System
Figure BDA0002577530700000055
When the system is disconnected at t-0.5 s, the inertia of the system is provided by the generator sets G-30 and G-38 together, and the actual inertia M of the system is provided at the momenteq＝M30+M38153 s; setting a frequency start threshold fsh59.9Hz, a time window length Delta T of 500ms, and a termination time T is calculatedend40 s. Then, the sliding time window Δ T is continued until the end time TendFor the inertia value M in the transient process of the upper half isolated network systemiAnd observing and drawing an inertia observed value distribution diagram of the system. Finally, according to the distribution rule of the system inertia observed value, the probability distribution curve is fitted through normal fitting, as shown in FIG. 4, so that an accurate estimated value M of the system inertia is obtainedeq153.87s, which is very close to the system true inertia value 153 s. Therefore, the method can effectively reduce the influence of adverse factors such as calculation errors, calculation noise, random fan output and the like, and accurately estimate the system inertia.

Claims (4)

1. The new energy power system inertia estimation method based on probability statistics is characterized by comprising the following steps of:
step 1: setting a frequency start threshold fshThe sliding time window length DeltaT and the calculation termination time Tend；
Step 2: judging whether the current new energy power system frequency reaches a frequency starting threshold value fshIf the frequency reaches the frequency starting threshold value, the moment when the frequency of the new energy power system reaches the frequency starting threshold value is recorded as t1And let t2＝t1+ Δ T; otherwise, repeating the step 2;
and step 3: respectively obtaining t through real-time measurement1And t2Active power output P of all wind power plants of new energy power system at two momentsWiActive demand P of each loadLiAnd the frequency change rate of the new energy power system
Figure FDA0003265555870000011
And 4, step 4: expanding the rotor motion equation of the synchronous machine into a form containing fan output, and applying t1,t2The rotor motion equations at two moments are subjected to difference making to obtain an inertia observed value M of the new energy power systemi；
And 5: the sliding time window is continued for a length deltaT until the end time TendObserving the inertia of the new energy power system obtained in the whole transient process;
step 6: counting inertia observed values of the new energy power system obtained through calculation in the transient process, and drawing an inertia observed value distribution diagram of the new energy power system;
and 7: obtaining an accurate estimated value M of the inertia of the new energy power system by fitting a probability distribution curve according to the distribution rule of the inertia observed values of the new energy power systemeq。
2. The new energy power system inertia estimation method based on probability statistics as claimed in claim 1, wherein the expanded synchronous machine rotor motion equation containing the fan output form is as shown in formula (1):
Figure FDA0003265555870000012
in the formula: m is the sum of inertia of all synchronous units of the new energy power system; pWThe sum of the active power output of all fans in the new energy power system; pmThe sum of the mechanical power of all synchronous machines in the new energy power system; pLThe sum of the active demands of all loads in the new energy power system; pLossThe network loss of the new energy power system; f. ofcoiThe frequency of the inertia center of the new energy power system.
3. The new energy power system inertia estimation method based on probability statistics as claimed in claim 2, wherein the inertia observed value M of the new energy power systemiThe calculation formula (2) is shown as follows:
Figure FDA0003265555870000021
in the formula: miIs the observed inertia value of the system.
4. The new energy power system inertia estimation method based on probability statistics as claimed in claim 1, wherein the frequency start threshold fshThe value of (1) is calculated by referring to the action threshold value of the first low-frequency load shedding wheel, the value range of the sliding time window length delta T is 300-500ms, and the termination time T is calculatedendThe value range of (A) is 30-50 s.
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