CN110518634A - Wind-powered electricity generation field control method is accessed based on the batteries to store energy system for improving exponential smoothing - Google Patents
Wind-powered electricity generation field control method is accessed based on the batteries to store energy system for improving exponential smoothing Download PDFInfo
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Abstract
The invention discloses access wind-powered electricity generation field control method based on the batteries to store energy system for improving exponential smoothing, comprising the following steps: S1, obtains wind-powered electricity generation historical data, predicts subsequent period output power using exponential smoothing is improved；S2, it calculates whether wind-powered electricity generation output pulsation rate meets professional standard, if do not met, is transferred to step S3；If met, terminate；S3, storage battery charge state and stability bandwidth standard value are considered for constraint condition, control energy-storage system of accumulator carries out charge and discharge.The present invention introduces APSO algorithm during exponential smoothing wind power fluctuates, and realizes the adaptive selection of smoothing factor, improves the effect of traditional secondary exponential smoothing.Simulation result shows in determining stored energy capacitance wind power plant, control strategy of the invention to stabilize effect more preferable.
Description
Technical field
It is especially a kind of based on the battery storage for improving exponential smoothing the present invention relates to wind power plant control technology field
It can system's access wind-powered electricity generation field control method.
Background technique
Wind-powered electricity generation is the important composition part of China's energy strategy, has been rapidly developed, has provided at present for social development
A large amount of clean energy resource.Influence due to wind-powered electricity generation vulnerable to factors such as weathers and environment, power output have biggish randomness, wave
Dynamic property, and there is a certain error for wind-powered electricity generation prediction at present, grid-connected wind power is conveying the same of a large amount of clean energy resourcies for power grid
When larger impact also is caused to the power supply quality of electric system and safe operation, seriously constrain the extensive of wind-power electricity generation
Using.How Power Output for Wind Power Field effectively stabilize and be of great significance.Energy-storage units are equipped in wind power plant,
The fluctuation of Power Output for Wind Power Field can be effectively reduced, to a certain extent wind-powered electricity generation is converted to schedulable power supply, facilitates
Reduce impact of the wind-power electricity generation to electric system.Battery, which has, safeguards that simple, long service life, quality are stable, high reliablity
The characteristics of.Along with the development of battery technology and power electronic technique, energy-storage system of accumulator obtains in stabilizing wind-powered electricity generation fluctuation
It is widely applied.
Wherein exponential smoothing method is the important method of time series analysis, in economic forecasting, Application of Power Metering Instruments demand
Prediction, energy conservation etc. have a wide range of applications.Smoothing factor is one of the important parameter in exponential smoothing algorithm, it
Selection determine prediction accuracy.Smoothing factor is by being manually specified in conventional method, and disadvantage shows the artificial warp of dependence
It tests, have dynamic.Common smoothing factor algorithms of automatic optimization has: Fibonacci method, Fibonacci method, tangential method,
Dichotomy etc..These algorithms of automatic optimization can find out the relatively figure of merit of smoothing factor in a short time, but be only limitted to smoothing factor
The case where value is monopole value, when smoothing factor value is multiple extremum, these algorithms cannot be guaranteed that the value found out converges on most
Excellent smoothing factor.
Summary of the invention
The object of the present invention is to provide access wind power plant controlling party based on the batteries to store energy system for improving exponential smoothing
Method constructs wind-powered electricity generation-energy-storage system of accumulator (BESS) combined operation system, accesses wind power plant control for energy-storage system of accumulator
System strategy, controls energy-storage system using exponential smoothing, and the solution procedure process of smoothing factor introduces particle group optimizing
Algorithm realizes the Rational choice of smoothing factor, with the minimum constraint condition of Prediction sum squares (SSE) with the lotus of battery
The wind farm grid-connected stability bandwidth national standard of electricity condition is constraint, realizes the reasonable access of wind-powered electricity generation.
To achieve the above object, the present invention adopts the following technical solutions:
Wind-powered electricity generation field control method is accessed based on the batteries to store energy system for improving exponential smoothing, comprising the following steps:
S1, wind-powered electricity generation historical data is obtained, predicts subsequent period output power using exponential smoothing is improved；
S2, it calculates whether wind-powered electricity generation output pulsation rate meets professional standard, if do not met, is transferred to step S3；If symbol
It closes, terminates；
S3, storage battery charge state and stability bandwidth standard value are considered for constraint condition, control energy-storage system of accumulator carries out
Charge and discharge.
Further, the acquisition wind-powered electricity generation historical data predicts subsequent period output work using exponential smoothing is improved
Rate specifically includes:
Wind-powered electricity generation whole historical data is obtained, a smoothing prediction result is obtained by weighted average method；
It is evaluation index by using minimum Prediction sum squares, is realized with APSO algorithm secondary smooth
The smoothing factor of model solves, and completes subsequent period output power sequence prediction.
Further, described to realize that the smoothing factor of secondary smoothing model solves with APSO algorithm, under completion
One period output power sequence prediction, specifically includes:
Wind power plant original sampling data is inputted, population is initialized；
The adaptive value of each particle of Calculation Estimation solves group optimal solution pzyThe optimal solution of current position with particle
pdq；
Particle information is updated according to speed, position and weight factor formula；
The calculating of mutation probability is completed according to adaptive response variance calculated result；
The number of iterations judgement completes iteration and exports group optimal solution pzy；Otherwise continue iteration.
Further, whether the calculating wind-powered electricity generation output pulsation rate meets professional standard, specifically includes:
Calculate the energy-storage system of accumulator capacity of wind power plant outfit, 1min power swing limit value, the grid-connected wind of grid connected wind power
Whether the 10min power swing limit value of electricity meets following table:
。
Further, the consideration storage battery charge state and stability bandwidth standard value are constraint condition, control battery storage
Energy system carries out charge and discharge, specifically includes:
Control system judges that 1min the or 10min power swing of grid connected wind power is more than specified value, then energy-storage system of accumulator
Charge and discharge are carried out to stabilize the difference of target He practical wind power output power, energy-storage system of accumulator needs absorption or release
Power and the target output of system can be denoted as respectively:
Work as POut_W, k> Pout_S,kWhen, energy-storage system of accumulator absorbs power；Work as POut_W, k< Pout_S,k, when, battery
Energy-storage system delivered power, value size are to stabilize the difference of output and wind power.
Further, the target output of energy-storage system of accumulator needs to absorb or discharge power and system calculates,
It specifically includes:
The power data of wind power plant a certain period is inputted, power swing rate is calculated and needs battery to store up to stabilize fluctuation
The output of energy system absorbs performance number；
Output power fluctuation of wind farm after considering the output power of energy-storage system of accumulator, and judge fluctuation at this time
Whether rate meets the requirement of professional standard table, if not satisfied, the charge-discharge electric power of adjustment energy-storage system of accumulator, if satisfied,
Calculate the state-of-charge of battery at this time；
Judge whether storage battery charge state meets the requirements, if not satisfied, directly terminating；If satisfied, change in next step
In generation, until meeting termination condition, the output power and corresponding energy-storage system of accumulator needs that wind power plant is exported after iteration fill
Electricity or the capacity of electric discharge.
The effect provided in summary of the invention is only the effect of embodiment, rather than invents all whole effects, above-mentioned
A technical solution in technical solution have the following advantages that or the utility model has the advantages that
The present invention introduces APSO algorithm during exponential smoothing wind power fluctuates, for control plan
Aiming at the problem that conventional particle group's algorithm, " particle " is possible near globally optimal solution " concussion " in solution procedure in slightly,
Inertia weight is adjusted by the way of adaptive, improves convergence.Realize the adaptive choosing of smoothing factor
It takes, improves the effect of traditional secondary exponential smoothing.Simulation result shows in determining stored energy capacitance wind power plant, of the invention
Control strategy to stabilize effect more preferable.
Detailed description of the invention
Fig. 1 is flow chart of the embodiment of the present invention；
Fig. 2 is the wind power plant operating system schematic diagram the present invention is based on energy-storage system of accumulator；
Fig. 3 is energy-storage system of accumulator control strategy schematic diagram of the present invention；
Fig. 4 is wind power undulated control flow graph of the present invention；
Fig. 5 is wind power plant original power curve of cyclical fluctuations figure；
Fig. 6 is that traditional secondary exponential smoothing stabilizes effect picture；
Fig. 7 is that improvement double smoothing based on APSO algorithm stabilizes effect picture；
Fig. 8 is basic double smoothing 1min stability bandwidth curve graph；
Fig. 9 is to improve double smoothing 1min stability bandwidth curve graph；
Figure 10 is traditional secondary exponential smoothing 10min stability bandwidth curve graph；
Figure 11 is to improve double smoothing 10min stability bandwidth curve graph.
Specific embodiment
In order to clarify the technical characteristics of the invention, below by specific embodiment, and its attached drawing is combined, to this
Invention is described in detail.Following disclosure provides many different embodiments or example is used to realize difference of the invention
Structure.In order to simplify disclosure of the invention, hereinafter the component of specific examples and setting are described.In addition, the present invention can
With repeat reference numerals and/or letter in different examples.This repetition is that for purposes of simplicity and clarity, itself is not
It indicates that the relationship between various embodiments and/or setting is discussed.It should be noted that illustrated component is not necessarily in the accompanying drawings
It is drawn to scale.Present invention omits the descriptions to known assemblies and treatment technology and process to avoid this is unnecessarily limiting
Invention.
As shown in Figure 1, based on the batteries to store energy system access wind-powered electricity generation field control method for improving exponential smoothing, including with
Lower step:
S1, wind-powered electricity generation historical data is obtained, predicts subsequent period output power using exponential smoothing is improved；
S2, it calculates whether wind-powered electricity generation output pulsation rate meets professional standard, if do not met, is transferred to step S3；If symbol
It closes, terminates；
S3, storage battery charge state and stability bandwidth standard value are considered for constraint condition, control energy-storage system of accumulator carries out
Charge and discharge.
As shown in Fig. 2, reasonably configuring energy-storage battery in wind power plant, by formulating control strategy, make itself and wind power plant
Coordinate power output, collectively forms wind-powered electricity generation-energy-storage system of accumulator combined operation system, Pout_BFor the charge and discharge of energy-storage system of accumulator
Electrical power, Pout_WFor Power Output for Wind Power Field, Pout_SCombine output work for wind-powered electricity generation-energy-storage system of accumulator combined operation system
Rate.Had by law of conservation of energy:
Pout_T=Pout_W+Pout_B (1)
Energy-storage system of accumulator can carry out charge and discharge as needed, present invention provide that Pout_B> 0 indicates batteries to store energy system
System electric discharge, if Pout_B< 0 then indicates that energy-storage system of accumulator charges, in a certain period, the charge and discharge of energy-storage system of accumulator
The limitation and the calculated relationship of SOC of power output are as follows:
In formula: △ t is sampling time interval, and value of the present invention is 10min, 1min；Pcmaxb_B, Pdmaxb_BAnd Pclim_B(n),
Pdlim_B(n)Respectively indicate the limit value of charge-discharge electric power in the charge-discharge electric power extreme value and a certain period of energy-storage system；Pn_B(n)With
En_BThe respectively rated value of system power and capacity；Smax_BAnd Smin_BFor the upper lower limit value of system SOC；SB(n-1)And SB(n)To work as
The SOC value of a preceding and upper time state；σBFor self discharge power；ηc_BAnd ηd_BThe respectively effect of energy-storage system charge and discharge process
Rate.
By configuring energy-storage system of accumulator in wind power plant, and coordination control strategy is formulated, by the output of whole system
Power Pout_TPower swing be limited to it is reasonable within the scope of, meet the grid-connected requirement of wind power plant, be the extensive of wind power plant
It is grid-connected to create conditions.
In step S1, wind-powered electricity generation historical data is obtained, predicts subsequent period output power, tool using exponential smoothing is improved
Body includes:
Wind-powered electricity generation whole historical data is obtained, a smoothing prediction result is obtained by weighted average method；
It is evaluation index by using minimum Prediction sum squares, is realized with APSO algorithm secondary smooth
The smoothing factor of model solves, and completes subsequent period output power sequence prediction.
The flat method of index obtains prediction knot based on the whole historical datas for predicting object, through average weighted mode
Fruit, recurrence formula are as follows:
In formula: α is smoothing factor, and n is Smoothness Index, xt+1For predicted value, xt, xt-1…xt-nFor observation.
By above formula: in the solution procedure of predicted value, the size of α can be determined during predicting shared by each observation data
Ratio, i.e. smoothing factor can cause large effect to sharpening result.The present embodiment is on the basis of primary smooth, by pre-
Double smoothing prediction is realized in the adjustment of measured value.The model of double smoothing is as follows:
Fundamental formular
Predictor formula
Wherein Prediction Parameters
From the above equation, we can see that by smoothly exporting xt (1)And xt (2)The calculating of achievable relevant parameter, in formulaIt indicates to the
T+T value is predicted.By secondary smoothing model, influence predicted value because being known as:
1. smoothing factor α, value size directly reflect the variation tendency of different time segment data.
2. initial value x0, engineering practice shows that observation time series is more, influence of the initial value to prediction result can be ignored,
The present invention meets this feature, that is, ignores initial value x0Influence to rear prediction result.
The present invention refers to based on above-mentioned prediction model, by using minimum Prediction sum squares (SSE) as evaluation
Mark realizes the solution of smoothing factor with APSO algorithm, completes the secondary smooth of whole event sequence.
Particle swarm algorithm (partical swarm optimization, PSO) is on the basis to birds predation research
On the random evolution calculation method that develops.The algorithm, which has, to be easily achieved, fast convergence rate, feature with high accuracy,
It is widely used in engineering practice.
In each iterative process, each particle is intended to complete the update of position and speed, updates rule are as follows:
In formula: pdq kFor the optimal location of current particle；pzy kFor the history optimal location of population；c1And c2For study because
Son；ω is inertia weight；R1 and r2 is the random number between 0 to 1.
In calculating process, inertia weight ω plays the role of being that particle keeps movement, guarantees that its expanded search space becomes
Gesture.In application process, particle is possible near globally optimal solution " shake " receipts for improving algorithm in order to solve this problem
Holding back property, the present invention are adjusted inertia weight by the way of adaptive, subtract it linearly during algorithm iteration
It is few, it may be assumed that
ω=ωmax-nc(ωmax-ωmin)/ncmax (9)
In formula: ωmin、ωmaxThe extreme value of inertia weight ω respectively；ncAnd ncmaxCurrent iteration number and greatest iteration respectively
Number.
Particle swarm algorithm is easily trapped into locally optimal solution, carry out process of the present invention in algorithm in iterative process
In the mutation probability of current optimal particle is realized with Colony fitness variance and current optimal solution size.It is operated by this, algorithm
Jumping out after can be achieved to fall into locally optimal solution, avoids the appearance of premature convergence problem.
During the progress of algorithm, calculating the variation tendency of the fitness value of particles all in group can be real
Quantitative analysis, Colony fitness variance now are carried out to the aggregation extent of each particle are as follows:
In formula: n is total population；fiFor the adaptive response of i-th of particle；faFor being averaged for current population fitness
Value；σ2For Colony fitness variance；F is for limiting σ2The calibration factor of size, its calculation formula is:
By above formula: σ2Directly reflect " convergence " degree of entire population；Its value is smaller, and population tends to restrain；Instead
It, entire population is in the stage of stochastic convergence.
It is stagnated in order to avoid search occurs in entire population, the present invention determines grain with the size of Colony fitness variance
" variation " mutation probability P of the subgroup to new directionkCalculation formula[13]It is as follows:
In formula: PkMake the mutation probability of global optimum for kth time iterative process；σkFor in kth time iteration, group from
Fitness variance；PmaxAnd PminThe respectively extreme value of mutation probability.
The present invention is to pk zyMutation operation during introduce random perturbation, its calculation formula is:
In formula: η is stochastic variable, meets Guass (0,1) distribution.
Based on APSO algorithm, to the solution procedure of smoothing factor are as follows:
1) it initializes, input observation data, initialization algorithm primary condition determines the overall size Q of population, iteration time
Number maximum value N, inertia weight ω and Studying factors c1、c2。
2) random particles for generating feasible zone, complete the initial position x of each particlei, initial velocity viSetting, and will
Each primary individual optimal solution and globally optimal solution are set as sufficiently large value.
3) to the particle 2) generated, the adaptive value of each particle is calculated, wherein the smallest value will be determined as group optimal solution
pzy, concurrently set pdqFor the optimal solution of the current position of particle.
4) counter updates, and calculates inertia weight according to formula (9), completes particle position x according to formula (8)iWith speed vi's
It calculates；The particle to cross the border can be handled according to boundary mutation strategy in calculating process, even vi> vmaxThen vi=vmaxIf
vi<-vmax, then vi=-vmax。
5) particle adaptive value reappraises, by the optimal solution p of current individualdqWith the adaptive value f of each particles (xi) into
Row compares, if fs(xi)<pdq, then pdq=fs(xi), xpi=xi；If fsmin<pzy, i.e., this generation group optimal solution is less than previous generation group
Body optimal solution, then pzy=fsmin。
6) population mutation operation.According to the calculated result of adaptive response variance, mutation probability is completed according to formula (12)
It calculates, and generates random number r ∈ [0,1], if r < Pk, then mutation operation is completed according to formula (13), otherwise goes to step 6).
7) the number of iterations judges, if completing iteration then goes to step 8), otherwise goes to step 4).
8) optimal solution p is completedzyOutput.
Wind-power electricity generation has intermittent and fluctuation, grid-connected to impact to the stability of operation of power networks.In order to drop
It is influenced caused by low wind-electricity integration, it is necessary to its power swing is stabilized, the regulation of electric system is accessed according to wind power plant,
The fluctuation of its maximum power is as shown in the table:
The wind farm grid-connected power fluctuation limit national standard of table 1
Tab.1 The national standard of active power in wind farm
It in step S2, calculates whether wind-powered electricity generation output pulsation rate meets professional standard, specifically includes: calculating wind power plant and be equipped with
Energy-storage system of accumulator capacity, the 1min power swing limit value of grid connected wind power, grid connected wind power 10min power swing limit value
Whether table 1. is met
As shown in figure 3, being equipped with energy-storage system of accumulator in wind power plant, need to control energy-storage system of accumulator,
Pout_WFor wind power plant t moment output power；Pout_SFor the power output value of smoothed calculating；Pout_cFor the output of whole system
The difference of power and wind power output power；Pout_TFor the output power of whole system；δ is wind power stability bandwidth.
When control system monitors that 1min the or 10min power swing of grid connected wind power is more than specified value, then batteries to store energy
System carries out charge and discharge to stabilize the difference of target He practical wind power output power, to realize output power fluctuation of wind farm
The target output of the purpose of inhibition, power and system that energy-storage system of accumulator needs to absorb or discharge can be denoted as respectively:
Work as POut_W, k> Pout_S,kWhen, energy-storage system of accumulator absorbs power, POut_W, k< Pout_S,k, when, battery storage
Energy system delivered power, value size is to stabilize the difference of output and wind power.
It is as shown in Figure 4 according to the fluctuation of wind farm grid-connected maximum power and system smoothness requirements, control flow: in Fig. 4: N
For maximum number of iterations, δTEIt is TEPower swing, δup TEIt is TEThe maximum value of power swing in time.The input of Fig. 4 is wind
The power data of electric field a certain period, calculates the stability bandwidth of these power, while being calculated as stabilizing these fluctuations and needing battery
The output of energy-storage system absorbs performance number, the wind power plant output after considering the output power of energy-storage system of accumulator in next step
Power swing, and judge whether stability bandwidth at this time meets the requirement of table 1, if not satisfied, adjusting filling for energy-storage system of accumulator
Discharge power if meeting the state-of-charge (SOC) for calculating battery at this time, and performs the next step, and carries out the charged shape of battery
The judgement of state is unsatisfactory for requiring, directly terminate, if meeting the requirements, next step iteration is carried out, until meeting termination condition.Iteration
After the output power of wind power plant can be obtained and corresponding energy-storage system of accumulator needs the capacity of charge or discharge.
It is illustrated below with specific example.
Data simulation analysis is carried out by certain external Large Scale Wind Farm Integration measured data, specified grid-connected 35 MW of power is equipped with
Energy-storage system of accumulator capacity is 10MW, and the sampling period of system is 1min, and the SOC value range of energy-storage system of accumulator is
[0.3,1].The initial power curve of cyclical fluctuations is as shown in Figure 5:
This example is based on national wind-electricity integration standard, respectively in two ways to the original grid-connected power of wind power plant
It is stabilized, it is as shown in Figure 6,7 to stabilize result.
For the comparison for preferably obtaining stabilizing effect, smoothed out 1min and 10min stability bandwidth is calculated, respectively such as
Shown in Fig. 8 and Fig. 9.
2 smooth effect of table compares
Table 2 gives basic double smoothing and the double smoothing proposed by the present invention that improves stabilizes Contrast on effect, has
Table 2 and Fig. 6 to Figure 11 can be observed, can smoothly reduce wind-powered electricity generation by secondary in the wind power plant for being equipped with certain stored energy capacitance
The grid-connected power swing in field, 1min and 10min power swing can meet wind-electricity integration national standard.And base proposed by the present invention
More preferable compared to traditional secondary exponential smoothing in the effect of the double smoothing of APSO algorithm, this has turned out this
Invent the correctness and validity of the control strategy proposed.
Above-mentioned, although the foregoing specific embodiments of the present invention is described with reference to the accompanying drawings, not protects to the present invention
The limitation of range, those skilled in the art should understand that, based on the technical solutions of the present invention, those skilled in the art
Member does not need to make the creative labor the various modifications or changes that can be made still within protection scope of the present invention.
Claims (6)
1. accessing wind-powered electricity generation field control method based on the batteries to store energy system for improving exponential smoothing, characterized in that including following step
It is rapid:
S1, wind-powered electricity generation historical data is obtained, predicts subsequent period output power using exponential smoothing is improved；
S2, it calculates whether wind-powered electricity generation output pulsation rate meets professional standard, if do not met, is transferred to step S3；If met, knot
Beam；
S3, storage battery charge state and stability bandwidth standard value are considered for constraint condition, control energy-storage system of accumulator carries out charge and discharge
Electricity.
2. wind-powered electricity generation field control method is accessed based on the batteries to store energy system for improving exponential smoothing as described in claim 1,
It is characterized in, the acquisition wind-powered electricity generation historical data, predicts subsequent period output power using exponential smoothing is improved, specifically include:
Wind-powered electricity generation whole historical data is obtained, a smoothing prediction result is obtained by weighted average method；
It is evaluation index by using minimum Prediction sum squares, secondary smoothing model is realized with APSO algorithm
Smoothing factor solves, and completes subsequent period output power sequence prediction.
3. wind-powered electricity generation field control method is accessed based on the batteries to store energy system for improving exponential smoothing as claimed in claim 2,
It is characterized in, it is described to realize that the smoothing factor of secondary smoothing model solves with APSO algorithm, complete subsequent period output
Power sequence prediction, specifically includes:
Wind power plant original sampling data is inputted, population is initialized；
The adaptive value of each particle of Calculation Estimation solves group optimal solution pzyThe optimal solution p of current position with particledq；
Particle information is updated according to speed, position and weight factor formula；
The calculating of mutation probability is completed according to adaptive response variance calculated result；
The number of iterations judgement completes iteration and exports group optimal solution pzy；Otherwise continue iteration.
4. wind-powered electricity generation field control method is accessed based on the batteries to store energy system for improving exponential smoothing as described in claim 1,
It is characterized in, whether the calculating wind-powered electricity generation output pulsation rate meets professional standard, it specifically includes:
Calculate energy-storage system of accumulator capacity that wind power plant is equipped with, the 1min power swing limit value of grid connected wind power, grid connected wind power
Whether 10min power swing limit value meets following table:
。
5. wind-powered electricity generation field control method is accessed based on the batteries to store energy system for improving exponential smoothing as claimed in claim 4,
It is characterized in, the consideration storage battery charge state and stability bandwidth standard value are constraint condition, and control energy-storage system of accumulator carries out
Charge and discharge specifically include:
Control system judges that 1min the or 10min power swing of grid connected wind power is more than specified value, then energy-storage system of accumulator carries out
Charge and discharge to stabilize the difference of target He practical wind power output power, power that energy-storage system of accumulator needs to absorb or discharge and
The target output of system can be denoted as respectively:
Work as POut_W, k> Pout_S,kWhen, energy-storage system of accumulator absorbs power；Work as POut_W, k <Pout_S,k,When, energy-storage system of accumulator
Delivered power, value size are to stabilize the difference of output and wind power.
6. wind-powered electricity generation field control method is accessed based on the batteries to store energy system for improving exponential smoothing as claimed in claim 5,
It is characterized in, the target output of power and system that energy-storage system of accumulator needs to absorb or discharge calculates, it specifically includes:
The power data of wind power plant a certain period is inputted, power swing rate is calculated and needs energy-storage system of accumulator to stabilize fluctuation
Output or absorb performance number；
Output power fluctuation of wind farm after considering the output power of energy-storage system of accumulator, and whether judge stability bandwidth at this time
Meet the requirement of professional standard table, if not satisfied, the charge-discharge electric power of adjustment energy-storage system of accumulator, if satisfied, calculating this
When battery state-of-charge；
Judge whether storage battery charge state meets the requirements, if not satisfied, directly terminating；If satisfied, carrying out next step iteration, directly
To termination condition is met, the output power and corresponding energy-storage system of accumulator that wind power plant is exported after iteration need to charge or put
The capacity of electricity.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111030140A (en) * | 2019-12-26 | 2020-04-17 | 国网内蒙古东部电力有限公司经济技术研究院 | Battery energy storage power grid frequency stability control method based on big data |
| CN111162551A (en) * | 2020-01-15 | 2020-05-15 | 国网内蒙古东部电力有限公司 | Storage battery charging and discharging control method based on wind power ultra-short term prediction |
| CN111242378A (en) * | 2020-01-15 | 2020-06-05 | 湖南日升工程咨询有限公司 | Engineering process dynamic cost control method based on BIM technology |
| CN117148171A (en) * | 2023-10-31 | 2023-12-01 | 新风光电子科技股份有限公司 | Energy storage battery data processing method and system |
| CN118074196A (en) * | 2024-04-17 | 2024-05-24 | 张家港格居信息科技有限公司 | Intelligent distribution method for energy of unstable power supply |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103580051A (en) * | 2013-11-19 | 2014-02-12 | 国家电网公司 | Wind storage system battery electrical charge state optimizing control system taking load characteristics into consideration |
| CN103928938A (en) * | 2014-02-24 | 2014-07-16 | 国家电网公司 | Stored energy power station optimizing control method with power prediction considered |
-
2019
- 2019-08-19 CN CN201910762469.1A patent/CN110518634A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103580051A (en) * | 2013-11-19 | 2014-02-12 | 国家电网公司 | Wind storage system battery electrical charge state optimizing control system taking load characteristics into consideration |
| CN103928938A (en) * | 2014-02-24 | 2014-07-16 | 国家电网公司 | Stored energy power station optimizing control method with power prediction considered |
Non-Patent Citations (3)
| Title |
|---|
| JIANZHOU WANG等: ""Combined modeling for electric load forecasting with adaptive particle swarm optimization"", 《ENERGY》 * |
| 王耀雷 等: ""基于自适应粒子群算法的直流微网能量优化管理"", 《现代电力》 * |
| 谢桦 等: ""基于指数平滑法的平抑风电功率波动储能控制策略"", 《北京交通大学学报》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111030140A (en) * | 2019-12-26 | 2020-04-17 | 国网内蒙古东部电力有限公司经济技术研究院 | Battery energy storage power grid frequency stability control method based on big data |
| CN111162551A (en) * | 2020-01-15 | 2020-05-15 | 国网内蒙古东部电力有限公司 | Storage battery charging and discharging control method based on wind power ultra-short term prediction |
| CN111242378A (en) * | 2020-01-15 | 2020-06-05 | 湖南日升工程咨询有限公司 | Engineering process dynamic cost control method based on BIM technology |
| CN111242378B (en) * | 2020-01-15 | 2023-09-15 | 正茂日升工程咨询有限公司 | Engineering process dynamic cost management and control method based on BIM technology |
| CN117148171A (en) * | 2023-10-31 | 2023-12-01 | 新风光电子科技股份有限公司 | Energy storage battery data processing method and system |
| CN117148171B (en) * | 2023-10-31 | 2024-01-26 | 新风光电子科技股份有限公司 | Energy storage battery data processing method and system |
| CN118074196A (en) * | 2024-04-17 | 2024-05-24 | 张家港格居信息科技有限公司 | Intelligent distribution method for energy of unstable power supply |
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