CN111342501B - Reactive power control method for microgrid with distributed power supply - Google Patents
Reactive power control method for microgrid with distributed power supply 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
- 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
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/50—Controlling the sharing of the out-of-phase component
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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
- H02J3/18—Arrangements for adjusting, eliminating or compensating reactive power in networks
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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
- H02J3/28—Arrangements for balancing of the load in a network by storage of energy
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- 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
- Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
- Y02E40/30—Reactive power compensation
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- 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
- Y02E70/00—Other energy conversion or management systems reducing GHG emissions
- Y02E70/30—Systems combining energy storage with energy generation of non-fossil origin
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Abstract
The invention relates to the technical field of power distribution networks, in particular to a reactive power control method of a microgrid with distributed power sources, which comprises the following steps: A) reading an active power predicted value and a reactive power predicted value, and an output predicted value of the wind and light power station; B) calculating a characteristic value, and if the characteristic value is smaller than a set threshold value, outputting active power only by the wind-solar power station, otherwise, outputting the active power and reactive power; C) calculating the deviation probability of the wind and light power station; D) and monitoring the output of the wind and light power station in real time, and scheduling the energy storage station and the wind and light power station according to the deviation probability. The substantial effects of the invention are as follows: by reasonably scheduling reactive power output, the stability of the microgrid can be improved, the cost of newly adding traditional energy sources for adapting to the access of renewable energy sources is obviously reduced, the access of the renewable energy sources is favorably expanded, the microgrid with high permeability and renewable energy sources is suitable, and the running stability of the power distribution network is improved.
Description
Technical Field
The invention relates to the technical field of power distribution networks, in particular to a reactive power control method for a microgrid with a distributed power supply.
Background
The distributed power supply refers to a power supply with a voltage grade of 35kV or below, which is not directly connected with a centralized power transmission system, and mainly comprises power generation equipment and an energy storage device. The distributed power generation can widely utilize clean renewable energy sources, and reduce the consumption of fossil energy and the emission of harmful gases. And the distributed power generation is positioned at the user side and close to the load center, so that the construction cost and the loss of the transmission and distribution network are greatly reduced. The novel energy production system has the advantages of low pollution emission, flexibility, convenience, high reliability and high efficiency. Distributed Generation (DG) devices can be classified into cogeneration, internal combustion engine set Generation, gas turbine Generation, small hydroelectric power Generation, wind power Generation, solar photovoltaic power Generation, fuel cells, and the like, depending on the technology used. At present, with the rapid development of wind power and photovoltaic power generation, the power distribution network in partial areas has the trend of gradually increasing the density of wind and photovoltaic station type distributed power supplies, and the proportion of the distributed power supplies in the power supply of the power distribution network is large. Wind and photoelectric stations are clean energy sources, and cannot damage the environment during power supply, so that the wind and photoelectric stations are mainly used for outputting active power. However, when the permeability is high, the power factor of the conventional energy equipment such as the transformer works in an unreasonable range, and the working efficiency and the working stability of the equipment such as the transformer are reduced. At present, the stable operation of the power grid is a main factor for limiting the further expansion of the scale of the wind and photovoltaic station. Therefore, a control method suitable for a high-penetration distribution network needs to be researched.
For example, chinese patent CN105470991B, published 2018, 8, 28, an output current-limiting control method for an inverter-type distributed power supply, which can ensure the maximization of the output active power of the inverter-type distributed power supply both under symmetric faults and asymmetric faults, and at the same time, improve the positive-sequence voltage amplitude of the output of the inverter-type distributed power supply, reduce the negative-sequence voltage amplitude, and reduce the asymmetry of the output voltage of the inverter-type distributed power supply, under different current-limiting methods: when the asymmetric fault occurs, the high voltage output level of the inverter type distributed power supply is ensured, so that the low-voltage protection action is not easy to trigger. Meanwhile, for the negative sequence overcurrent protection, the current limiting method provided by the method limits the output of the negative sequence current, so that the negative sequence overcurrent protection is not easy to trigger. But it can only solve the problem of grid stability degradation due to voltage and cannot solve the grid operation problem due to power factor.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the technical problem that the permeability of renewable energy sources in the existing power distribution network is not high is solved. A reactive power control method of a microgrid with distributed power supplies is provided, and equipment in the microgrid can work at a more reasonable power factor.
To solve the problems mentioned aboveThe technical problem is that the technical scheme adopted by the invention is as follows: a reactive power control method of a microgrid with a distributed power supply, wherein the distributed power supply comprises an energy storage station and a wind-solar power station, and the method comprises the following steps: A) dividing a day into n periods t j,j∈[1,n] Reading the next time interval t j+1 Active power prediction value of load
And reactive power prediction
Obtaining wind-solar power station G i,i∈[1,l] In the next time period t j+1 Predicted value of output
l is the number of wind and light power stations; B) calculating the next time period t j+1 Characteristic value
If the characteristic value
Less than a first set threshold lambda thr Then t is j+1 Wind-solar power station G in time interval i,i∈[1,l] Only active power is output, otherwise, calculation is carried out
Wind-light power station G i,i∈[1,l] Output active power
And reactive power
And satisfy
t j+1 In time period, wind-light power station G i Reactive power of
Keeping the same; C) wind and light power station G i,i∈[1,l] Real-time force of
Deviation from predicted value
Probability of deviation of
Tau is the rate of deviation and is the rate of deviation,
σ is the deviation rate τ | t j+1 The probability of occurrence; D) time period t j+1 Internal and real-time monitoring wind-light power station G i,i∈[1,l] Real-time force of
Deviation rate of (τ | t) j+1 If deviation probability
Then the energy storage station E is added i,i∈[1,m] Charging power or reducing energy storage station E i,i∈[1,m] M is the number of energy storage stations, otherwise, the wind-light power station G is increased according to a set range i,i∈[1,l] Output reactive power
Until real-time output
Deviation rate τ | t of (1) j+1 Corresponding deviation probability
Fall back to sigma thr The following. At present, a wind and light power station only outputs active power, so that the operation of a distribution network is easy to be unstable, the upper limit of the output of the wind and light power station is often limited, wind and light abandon is caused, and the access scale of the wind and light power station is also limited. By reasonably scheduling reactive power output, the method can improve the stability of the microgrid, obviously reduce the cost of newly adding traditional energy sources for adapting to the access of renewable energy sources, and is suitable for the microgrid with renewable energy sources with high permeability.
Preferably, in step B), the wind-solar power station G is predicted i The next time period t i+1 Output prediction
The method comprises the following steps: B11) enumerating influences on wind-solar power station G i,i∈[1,l] The factors of the output, constitute factor vectors; B12) selecting a wind-solar power station G i The output interval is set
Is equally divided into N small intervals, and the cell size is equal to the cell size,
for wind-solar power station G i Maximum output of (c); B13) wind-light power station G i Operating for a period of time to acquire the values of the factors in the factor vector and the wind-solar power station G at a set frequency i Output, collection of factor vectors and wind-light power station G i The output of the system is associated with the cells to form sample data; B14) establishing a neural network model, wherein the input of the neural network model is a factor vector, the output is the probability of N small intervals, and the neural network model is trained and verified by using the sample data obtained in the step B13); B15) in the next time period t i+1 Setting time before starting, measuring wind-light power station G i The value of the factor in the factor vector is input into the neural network model, and the mean value among the cells output by the neural network model is used as the output prediction
The value of (c). The ultra-short term output prediction of the wind and light power station has various published prediction models, the ultra-short term output prediction is obtained by the models according to the commonality of the wind and light power stations, the evaluation index can reflect the deviation between the model prediction result and the actual result under the actual condition, the difference between the wind and light power stations and the difference between the same wind and light power station in different time periods is reflected, the error of the model prediction is corrected, and the accuracy of the output prediction is improved.
Preferably, step B13) further comprises factor screening, comprising: B131) associating the factor vector with the corresponding wind-solar power station G i Form a correlation vector V g,g∈[1,M] ＝<a 1 ,a 2 ,…,a k ,S>M is the number of the collected factor vector values, k is the number of the factors, and the difference value of each factor and the output force is calculated among the associated vectors to form a difference value vector V g-k ＝<Δa 1 ,Δa 2 ,…,Δa k ,ΔS>Wherein g is [1, M ]],k∈[1,M]G ≠ k, difference vector V g-k Each element in the vector is an association vector V g And V k Difference values of corresponding elements to obtain at least M difference value vectors V g-k (ii) a B132) The difference vector V g-k Dividing the value of each element by the value corresponding to the output force, converting the output force into a unit 1, and obtaining a corrected difference vector
B133) Calculating a corrected difference vector V' g-k Standard deviation of each element in
If it is
If the value is larger than the first set threshold value, the corresponding factor a is used i And (4) screening. When the output of the wind and light power station is not changed greatly, irrelevant factors can reflect the characteristic of random fluctuation, and screening can be performed through standard deviation.
Preferably, in step D), the energy storage station E is controlled i,i∈[1,m] The method for increasing the charging power comprises the following steps: D11) computing energy storage station E i,i∈[1,m] Total increased charging power of
k is a set margin coefficient, k>1; D12) will be of period t j+1 Dividing the evaluation function into N small periods
Where z represents the number of small cycles,
for feeder i during time period t j+1 The average load of the z-th small period of time,
h is the number of feeders, i is the upper limit of the load of the feeder i; D13) at a set time before the beginning of the z-th small period, starting from the (z-1) th small period to the wind-light power station G within the current time i Real-time force of
Average value calculation of
A value of (d); D14) using an optimization algorithm, an evaluation function is obtained
Energy storage station E with the smallest value i,i∈[1,m] The charging power is increased in real time, and the process returns to step D13) at a predetermined timing before the start of the next small cycle. The energy storage equipment is scheduled and coordinated on site in a small period, and the energy utilization efficiency is improved.
Preferably, in step B), the wind-solar power station G i,i∈[1,l] Active power of
And reactive power
Distribution method packageComprises the following steps: B11) establishing an evaluation function
For feeder i during time period t j+1 The average active power transferred in-between,
for feeder i during time period t j+1 The average reactive power transferred in-between,
h is the number of feeders, i is the upper limit of the load of the feeder i; B12) using an optimization algorithm, an evaluation function is obtained
Minimum value wind-solar power station G i,i∈[1,l] Reactive power of
Value, active power
Preferably, in step B), the wind-solar power station is calculated
Operating power factor of
Time period t j+1 Internal active power
And reactive power
Is distributed to
Always true, λ' thr Is a second set threshold value, λ' thr >λ thr . The working state of the wind and light power station is ensured to be at a better level, and excessive renewable energy waste is avoided.
Preferably, in step C), the deviation probability is calculated
The method comprises the following steps: C11) will be of period t j Dividing into M small periods, and counting wind-light power station G in each small period i,i∈[1,l] Mean value of real-time forces of
r∈[1,M](ii) a C12) Will be of period t j+1 The former w time periods execute the step C11), and the wind and light power station G in each small period in each time period is obtained i,i∈[1,l] Mean value of real-time forces of
u∈[j-w,j],r∈[1,M](ii) a C13) Statistics of
Maximum and minimum values of
Section of will
Equally dividing the data into a plurality of value intervals, and respectively counting the data falling into each interval
Of each value interval
The ratio of the number of the wind-solar power station G to the number w.M is used as the wind-solar power station G i,i∈[1,l] Real-time force of
Deviation probability corresponding to falling value interval
Through deviation probability, the possible duration of the output fluctuation of the current wind and light power station is obtained, the deviation probability corresponding to the output level is large, the duration of the output level is longer, therefore, a scheduling mode more suitable for a long time needs to be adopted for the fluctuation output level, if the possibility that the duration of the output level is short is large, the influence of the output fluctuation can be eliminated through short-term compensation of an energy storage device, and flexible scheduling is achieved.
The substantial effects of the invention are as follows: by reasonably scheduling reactive power output, the stability of the microgrid can be improved, the newly increased cost of the traditional energy sources for adapting to the access of renewable energy sources is obviously reduced, the access of the renewable energy sources is favorably expanded, the microgrid with high permeability and renewable energy sources is suitable, and the running stability of the power distribution network is improved.
Drawings
Fig. 1 is a flowchart illustrating a method for controlling a reactive power according to an embodiment.
FIG. 2 is a flow chart of a wind-solar power plant output prediction method according to an embodiment.
Fig. 3 is a flowchart illustrating a method for increasing charging power of a power storage station according to an embodiment of the invention.
Detailed Description
The following provides a more detailed description of the present invention, with reference to the accompanying drawings.
The first embodiment is as follows:
a reactive power control method for a microgrid with distributed power sources, wherein the distributed power sources include an energy storage station and a wind-light power station, as shown in fig. 1, the embodiment includes the following steps:
A) dividing a day into n periods t j,j∈[1,n] Reading the next time interval t j+1 Active power prediction value of load
And reactive power prediction
Obtaining wind-solar power station G i,i∈[1,l] In the next time period t j+1 Predicted value of output
And l is the number of the wind-solar power stations.
B) Calculating the next time period t j+1 Characteristic value
If the characteristic value
Less than a first set threshold lambda thr Then t is j+1 Wind-solar power station G in time interval i,i∈[1,l] Only active power is output, otherwise, calculation is carried out
Wind-light power station G i,i∈[1,l] Output active power
And reactive power
And satisfy
t j+1 In time period, wind-light power station G i Of reactive power
Remain unchanged.
Wind-light power station G i,i∈[1,l] Active power of
And reactive power
The distribution method comprises the following steps: B11) establishing an evaluation function
For feeder i at time period t j+1 The average active power transferred in-between,
for feeder i at time period t j+1 The average reactive power transferred in-between,
h is the number of feeders, i is the upper limit of the load of the feeder i; B12) using an optimization algorithm, an evaluation function is obtained
Minimum value wind-solar power station G i,i∈[1,l] Of reactive power
Value, active power
Wind-solar power station G i,i∈[1,l] Operating power factor of
Time period t j+1 Internal active power
And reactive power
Is distributed to
Always true, λ' thr Is a second set threshold value, λ' thr >λ thr . The working state of the wind and light power station is ensured to be at a better level, and excessive renewable energy waste is avoided. As shown in fig. 2, predictive wind-solar plant G i The next time period t i+1 Output prediction
The method comprises the following steps: B11) enumerating influences on wind-solar power station G i,i∈[1,l] The factors of the output, constitute factor vectors; B12) selecting a wind-solar power station G i The output interval is set
Is equally divided into N small intervals,
for wind-solar power station G i The maximum output of (c); B13) wind-light power station G i Operating for a period of time to acquire values of factors in the factor vector and the wind and light power station G at a set frequency i The output of (1), collecting the factor vector and the wind-light power station G i The output of (2) is associated with the cells to form sample data; B14) establishing a neural network model, wherein the input of the neural network model is a factor vector, the output is the probability of N small intervals, and the neural network model is trained and verified by using the sample data obtained in the step B13); B15) in the next time period t i+1 Setting time before starting, measuring wind-light power station G i The value of the factor in the factor vector is input into the neural network model, and the mean value among the cells output by the neural network model is used as the output prediction
The value of (c). The ultra-short term output prediction of the wind and light power station has various published prediction models, but the ultra-short term output prediction is obtained by the models according to the commonality of the wind and light power station, and the evaluation index can reflect the actual outputAnd the deviation of the model prediction result and the actual result under the condition reflects the difference between the wind and light power stations and the difference of the same wind and light power station in different time periods, so that the error of the model prediction is corrected, and the accuracy of the output prediction is improved.
Step B13) further comprises factor screening comprising: B131) associating the factor vector with the corresponding wind-solar power station G i Form a correlation vector V g,g∈[1,M] ＝<a 1 ,a 2 ,…,a k ,S>M is the number of the collected factor vector values, k is the number of the factors, and the difference value of each factor and the output force is calculated among the associated vectors to form a difference value vector V g-k ＝<Δa 1 ,Δa 2 ,…,Δa k ,ΔS>Wherein g is [1, M ]],k∈[1,M]G ≠ k, difference vector V g-k Each element in the vector is an association vector V g And V k Difference values of corresponding elements to obtain at least M difference value vectors V g-k (ii) a B132) The difference vector V g-k Dividing the value of each element by the value corresponding to the output force, converting the output force into a unit 1, and obtaining a corrected difference vector
B133) Calculating a corrected difference vector V g ′ -k Standard deviation of each element in
If it is
If the value is larger than the first set threshold value, the corresponding factor a is used i And (6) screening. When the output of the wind and light power station is not changed greatly, irrelevant factors can reflect the characteristic of random fluctuation, and screening can be performed through standard deviation.
C) Wind and light power station G i,i∈[1,l] Real time force output
Deviation from predicted value
Deviation probability of (2)
Tau is the rate of deviation and is the rate of deviation,
σ is the deviation rate τ | t j+1 The probability of occurrence. Calculating the probability of departure
The method comprises the following steps: C11) will be of period t j Dividing into M small periods, and counting wind-light power station G in each small period i,i∈[1,l] Mean value of real-time forces
r∈[1,M](ii) a C12) Will be of period t j+1 The former w time periods execute the step C11), and the wind-solar power station G in each small period in each time period is obtained i,i∈[1,l] Mean value of real-time forces of
u∈[j-w,j],r∈[1,M](ii) a C13) Statistics of
Maximum and minimum values of
Section of will
Equally dividing the data into a plurality of value intervals, and respectively counting the data falling into each interval
Of each value interval
The ratio of the number of the wind-solar power station G to the number w.M is used as the wind-solar power station G i,i∈[1,l] Real-time force of
Deviation probability corresponding to falling value interval
Through deviation probability, the possible duration of the output fluctuation of the current wind and light power station is obtained, the deviation probability corresponding to the output level is large, the duration of the output level is longer, therefore, a scheduling mode more suitable for a long time needs to be adopted for the fluctuation output level, if the possibility that the duration of the output level is short is large, the influence of the output fluctuation can be eliminated through short-term compensation of an energy storage device, and flexible scheduling is achieved.
D) Time period t j+1 Internal and real-time monitoring wind and light power station G i,i∈[1,l] Real time force output
Deviation rate τ | t of (1) j+1 If the probability of deviation is high
Then add the energy storage station E i,i∈[1,m] Charging power or reducing energy storage station E i,i∈[1,m] M is the number of energy storage stations, otherwise, the wind-light power station G is increased according to a set range i,i∈[1,l] Reactive power of output
Until real-time output
Deviation rate of (τ | t) j+1 Corresponding deviation probability
Fall back to sigma thr The following. As shown in fig. 3, the energy storage station E is controlled i,i∈[1,m] The method for increasing the charging power comprises the following steps: D11) computing energy storage station E i,i∈[1,m] Total increased charging power of
k is a set margin coefficient, k>1; D12) will be of period t j+1 Dividing the evaluation function into N small periods
Where z represents a small cycle number and,
for feeder i during time period t j+1 The average load of the z-th small period of (c),
h is the number of feeders, i is the upper limit of the load of the feeder i; D13) at a set time before the beginning of the z-th small period, the wind-light power station G within the (z-1) th small period to the current time i Real-time force of
Mean value calculation of
A value of (d); D14) using an optimization algorithm, an evaluation function is obtained
Energy storage station E with the smallest value i,i∈[1,m] The charging power is increased in real time, and the process returns to step D13) at a predetermined timing before the start of the next small cycle. The energy storage equipment is scheduled and coordinated on site in a small period, and the energy utilization efficiency is improved.
At present, the wind and light power station only outputs active power, so that the operation of a distribution network is easy to be unstable, the upper limit of the output of the wind and light power station is often limited, wind and light abandon is caused, and the access scale of the wind and light power station is also limited. By reasonably scheduling reactive power output, the method can improve the stability of the microgrid, obviously reduce the cost of newly adding traditional energy sources for adapting to the access of renewable energy sources, and is suitable for the microgrid with renewable energy sources with high permeability.
The above-described embodiments are only preferred embodiments of the present invention, and are not intended to limit the present invention in any way, and other variations and modifications may be made without departing from the spirit of the invention as set forth in the claims.
Claims (7)
1. A reactive power control method of a microgrid with distributed power supplies, wherein the distributed power supplies comprise an energy storage station and a wind-solar power station,
the method comprises the following steps:
A) dividing a day into n periods t j,j∈[1,n] Reading the next time interval t j+1 Active power prediction value of load
And reactive power prediction
Obtaining a wind-solar power station G i,i∈[1,l] In the next time period t j+1 Predicted value of output
l is the number of wind and light power stations;
B) calculating the next time period t j+1 Characteristic value
If the characteristic value
Less than a first set threshold lambda thr Then t is j+1 Wind-solar power station G in time interval i,i∈[1,l] Only active power is output, otherwise, calculation is carried out
Wind-light power station G i,i∈[1,l] Output active power
And reactive power
And satisfy
t j+1 In time period, wind-light power station G i Reactive power of
Keeping the same;
C) wind and light power station G i,i∈[1,l] Real time force output
Deviation from predicted value
Probability of deviation of
Tau is the rate of deviation and is the rate of deviation,
σ is the deviation rate τ | t j+1 The probability of occurrence;
D) time period t j+1 Internal and real-time monitoring wind and light power station G i,i∈[1,l] Real time force output
Deviation rate of (τ | t) j+1 If deviation probability
Then the energy storage station E is added i,i∈[1,m] Charging power or reducing energy storage station E i,i∈[1,m] M is the number of energy storage stations, otherwise, the wind-light power station G is increased according to a set range i,i∈[1,l] Reactive power of output
Until real-time output
Deviation rate of (τ | t) j+1 Corresponding deviation probability
Fall back to sigma thr The following.
2. The reactive power control method of claim 1, wherein the reactive power control method comprises the steps of,
in step B), the wind and light power station G is predicted i The next time period t i+1 Output prediction
The method comprises the following steps:
B11) enumerating influences on wind-solar power station G i,i∈[1,l] The factors of the output, constitute factor vectors;
B12) selecting a wind-solar power station G i Applying a force to the interval
Is equally divided into N small intervals,
for wind-solar power station G i The maximum output of (c);
B13) wind-light power station G i Operating for a period of time to acquire the values of the factors in the factor vector and the wind-solar power station G at a set frequency i Output, collection of factor vectors and wind and lightPower station G i The output of (2) is associated with the cells to form sample data;
B14) establishing a neural network model, wherein the input of the neural network model is a factor vector, the output is the probability of N small intervals, and the neural network model is trained and verified by using the sample data obtained in the step B13);
B15) in the next time period t i+1 Setting the time before starting, measuring the wind-light power station G i The value of the factor in the factor vector is input into the neural network model, and the mean value among the cells output by the neural network model is used as the output prediction
The value of (c).
3. The method of claim 2, wherein the reactive power control of the microgrid comprising a distributed power source is carried out,
step B13) further comprises factor screening comprising:
B131) associating the factor vector with the corresponding wind-solar power station G i Form a correlation vector V g,g∈[1,M] ＝<a 1 ,a 2 ,…,a k ,S>M is the number of the collected factor vector values, k is the number of the factors, and the difference value of each factor and the output force is calculated among the associated vectors to form a difference value vector V g-k ＝<Δa 1 ,Δa 2 ,…,Δa k ,ΔS>Wherein g is [1, M ]],k∈[1,M]G ≠ k, difference vector V g-k Each element in the vector is an association vector V g And V k Difference values of corresponding elements to obtain at least M difference value vectors V g-k ；
B132) The difference vector V g-k Dividing the value of each element by the value corresponding to the output force, converting the output force into a unit 1, and obtaining a corrected difference vector
B133) Calculating a corrected difference vector V' g-k Standard deviation of each element in
If it is
If the value is larger than the first set threshold value, the corresponding factor a is used i And (6) screening.
4. The reactive power control method comprising a distributed power microgrid of claim 1, 2 or 3,
in step D), the energy storage station E is controlled i,i∈[1,m] The method for increasing the charging power comprises the following steps:
D11) computing energy storage station E i,i∈[1,m] Total increased charging power of
k is a set margin coefficient, k>1；
D12) Will be of period t j+1 Dividing into N small periods, and establishing evaluation function
Where z represents a small cycle number and,
for feeder i during time period t j+1 The average load of the z-th small period of (c),
h is the number of feeders, i is the upper limit of the load of the feeder i;
D13) at a set time before the beginning of the z-th small period, the wind-light power station G within the (z-1) th small period to the current time i Real time force output
Mean value calculation of
A value of (d);
D14) using an optimization algorithm, an evaluation function is obtained
Energy storage station E with the smallest value i,i∈[1,m] The charging power is increased in real time, and the process returns to step D13) at a predetermined timing before the start of the next small cycle.
5. The reactive power control method comprising a distributed power microgrid of claim 1, 2 or 3,
in step B), wind-solar power station G i,i∈[1,l] Active power of
And reactive power
The distribution method comprises the following steps:
B11) establishing an evaluation function
For feeder i at time period t j+1 The average active power transferred in-between,
for feeder i at time period t j+1 The average reactive power transferred in-between,
h is the number of feeders, i is the upper limit of the load of the feeder i;
B12) using an optimization algorithm, an evaluation function is obtained
Minimum value wind-solar power station G i,i∈[1,l] Reactive power of
Value, active power
6. The reactive power control method comprising a distributed power microgrid of claim 1, 2 or 3,
in step B), calculating the wind-light power station G i,i∈[1,l] Operating power factor of
Time period t j+1 Internal active power
And reactive power
Is distributed to
Always true, λ' thr Is a second set threshold value, λ' thr >λ thr 。
7. The reactive power control method comprising a distributed power microgrid of claim 1, 2 or 3,
in step C), the deviation probability is calculated
The method comprises the following steps:
C11) will be of period t j Dividing into M small periods, and counting wind-light power station G in each small period i,i∈[1,l] Mean value of real-time forces
C12) Will be of period t j+1 The former w time periods execute the step C11), and the wind-solar power station G in each small period in each time period is obtained i,i∈[1,l] Mean value of real-time forces of
C13) Statistics of
Maximum and minimum values of
Section of will
Equally dividing the data into a plurality of value intervals, and respectively counting the data falling into each interval
Of each value interval
The ratio of the number of the wind-solar power station G to the number w.M is used as the wind-solar power station G i,i∈[1,l] Real-time force of
Deviation probability corresponding to falling value interval
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104638772A (en) * | 2013-11-14 | 2015-05-20 | 国家电网公司 | Battery energy storage power station energy management method based on wind power prediction |
| CN107069807A (en) * | 2017-03-22 | 2017-08-18 | 广西大学 | Containing uncertain budget adjust without balance nodes microgrid Robust Scheduling method |
| KR101786904B1 (en) * | 2016-08-17 | 2017-11-15 | 인천대학교 산학협력단 | Model Predictive Control Apparatus and Method to BESS for Microgrid Control |
| CN109066744A (en) * | 2018-08-08 | 2018-12-21 | 中国电力科学研究院有限公司 | One kind coordinated scheduling method of power distribution network containing energy storage and system |
-
2020
- 2020-02-24 CN CN202010112982.9A patent/CN111342501B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104638772A (en) * | 2013-11-14 | 2015-05-20 | 国家电网公司 | Battery energy storage power station energy management method based on wind power prediction |
| KR101786904B1 (en) * | 2016-08-17 | 2017-11-15 | 인천대학교 산학협력단 | Model Predictive Control Apparatus and Method to BESS for Microgrid Control |
| CN107069807A (en) * | 2017-03-22 | 2017-08-18 | 广西大学 | Containing uncertain budget adjust without balance nodes microgrid Robust Scheduling method |
| CN109066744A (en) * | 2018-08-08 | 2018-12-21 | 中国电力科学研究院有限公司 | One kind coordinated scheduling method of power distribution network containing energy storage and system |
Non-Patent Citations (3)
| Title |
|---|
| Impacts_of_Stochastic_Wind_Power_and_Storage_Participation_on_Economic_Dispatch_in_Distribution_Systems;Abebe W. Bizuayehu;《IEEE TRANSACTIONS ON SUSTAINABLE ENERGY》;20160731;第1336-1345页 * |
| 考虑电压稳定约束的无功补偿优化配置;程振龙;《电力系统及其自动化学报》;20150430;第86-91页 * |
| 考虑风电消纳的配电网多目标无功协同优化;金尚婷;《广东电力》;20190131;第52-59页 * |
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