CN105260618B - A kind of energy-storage system typical curve method for digging based on cloud model - Google Patents

A kind of energy-storage system typical curve method for digging based on cloud model Download PDF

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CN105260618B
CN105260618B CN201510741815.XA CN201510741815A CN105260618B CN 105260618 B CN105260618 B CN 105260618B CN 201510741815 A CN201510741815 A CN 201510741815A CN 105260618 B CN105260618 B CN 105260618B
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CN105260618A (en
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韩晓娟
赵泽琨
籍天明
刘大贺
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North China Electric Power University
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Abstract

The invention belongs to power system energy-storage system technical field, more particularly to a kind of energy-storage system typical curve method for digging based on cloud model, selectes the determination application scenarios of energy-storage system and suitable control strategy, statistics energy-storage system longitudinal direction sequential power.The frequency distribution of energy-storage system longitudinal direction sequential performance number is decomposed into several varigrained cloud models, the expectation weighted sum to normal cloud model group, provides energy-storage system typical case's operation curve.The present invention can greatly reduce amount of calculation, be easy to that energy-storage system charge and discharge power is integrated with understanding cognition；Backward cloud generator in cloud model can be converted to precise information the cloud model represented with numerical characteristic, realize extraction and conversion of the numerical value to concept, that is, realize that energy-storage system charge and discharge power data excavates energy-storage system typical case's operation curve.

Description

A kind of energy-storage system typical curve method for digging based on cloud model
Technical field
The invention belongs to power system energy-storage system technical field, more particularly to a kind of energy-storage system allusion quotation based on cloud model Type curve method for digging.
Background technology
Fluctuation, intermittence and the unpredictability of renewable energy power generation can give the operation of existing power system to bring one Fixed impact.Energy-storage system has quick response and dynamic regulation ability, can effectively improve the friendly of New-energy power system.
The research for energy-storage system charge and discharge power all concentrates on optimal control and capacity configuration etc. at present, for allusion quotation The excacation concern of type operation curve is less, there is not clear enough to energy-storage system charge and discharge power overall cognitive, and calculates Measure the problems such as huge.Excavate and determine that energy-storage system typical case's operation curve under application scenarios is that energy-storage system operation characteristic is carried Take and summarize, it is possible to achieve for the overall cognitive of energy-storage system charge and discharge power situation, and then aid in solving asking for capacity configuration Topic.The typical operation curve extracted is utilized to save amount of calculation with amount of compressed data.
The content of the invention
The problem of existing for the research of current energy-storage system charge and discharge power, the present invention proposes a kind of storage based on cloud model Energy system typical curve method for digging, including：
Step 1, the application scenarios and control strategy for determining energy-storage system, the charge and discharge power to energy-storage system carry out data and adopted Collection, note jth day k moment energy-storage system charge and discharges performance number is Pjk, j=1,2 ..., n, n is to count total number of days, k=1,2 ..., s, s For daily sampled point number, k moment charge and discharge power maximum is P in n dayskmax, then mark of the energy-storage system charge and discharge power at the k moment The one value is：Pjk_pu=Pjk/Pkmax, Pjk_pu∈[0,1]；
Step 2, the perunit charge and discharge performance number at different moments for counting n days are simultaneously stored in matrix Ms*n=[p1,p2,…,pk,…, ps]T, pkIt is the perunit charge and discharge vector power at k moment in energy-storage system n days, pk=[P1k_pu,P2k_pu,…,Pnk_pu]；
Step 3, statistics perunit charge and discharge vector power pkFrequency distribution, obtain pkCurve of frequency distribution f (x)k, and root According to curve of frequency distribution f (x)kPeak FkjCorresponding abscissa obtains the expectation E of cloud modelxki, i.e. the position of centre of gravity of cloud；i =1,2 ..., m, initial value i=1；
Step 4, the expectation E with cloud modelxkiCentered on, first trough position of the right and left appearance is found out, is divided X is not designated as itleftWith xright, it is determined that the water dust scope d of first cloud modeli=min (Exki-xleft,xright-Exki)；Utilize Reverse cloud algorithm without degree of certainty information calculates entropy ETkiWith super entropy Heki, then calculate cloud model data distribution function fcloud-ki；
Step 5, from curve of frequency distribution f (x)kIn subtract cloud model distribution function fcloud-ki, obtain new frequency distribution Curve f ' (x)k, and find f ' (x)kPeak Fki+1；Calculate peak FkiWith peak Fki+1Difference Distanceki=Fki- Fki+1；
If step 6, DistancekiMore than or equal to threshold epsilon, then peak F is found outki+1Corresponding abscissa Exki+1, then i=i+ 1 and 4~step 5 of repeat step, until DistancekmLess than threshold epsilon, and obtain corresponding abscissa Exki；If DistancekiLess than threshold epsilon, then k=k+1, that is, enter the normal cloud model group for finding subsequent time；
Step 7, the representative charge and discharge performance number vector at k moment is obtained according to step 4~step 6：(Exk1,Exk2,…, Exki,…,Exkm)；Energy-storage system is as follows in the computational methods of the typical charge and discharge performance number at k moment：
Step 8, k+1 is calculated successively, k+2 ..., energy-storage system typical case's charge and discharge performance number at s moment, obtain energy-storage system allusion quotation Type operation curve P=[P1, P2..., Ps]。
Reverse cloud algorithm in the step 4 is：
Step 401, by scope in (Exk1-d1, Exk1+d1) in power data be designated as xk1i, i=1,2 ..., T；According to sample This xk1iSample is calculated it is expected：
Step 402, calculate sample variance:
Step 403, calculate entropy:
Step 404, calculate super entropy:
The data distribution function computational methods of cloud model in the step 4 are：
Beneficial effects of the present invention are as follows：Applied to peak load shifting, the plan of tracking wind-powered electricity generation is contributed, and stabilizes wind-powered electricity generation or photovoltaic The application scenarios such as the power swing of generating, amount of calculation can be greatly reduced, energy-storage system charge and discharge power can be integrated with understanding cognition； Backward cloud generator in cloud model can be converted to precise information the cloud model represented with numerical characteristic C (Ex, En He), Extraction and conversion of the numerical value to concept are realized, that is, it is bent to realize that energy-storage system charge and discharge power data excavates energy-storage system typical case's operation Line.
Brief description of the drawings
Fig. 1 is the energy-storage system typical curve mining algorithm flow chart based on cloud model；
Energy-storage system control strategy figure when Fig. 2 is the power swing for stabilizing photovoltaic plant；
Fig. 3 is the energy-storage system typical case's operation curve figure excavated in embodiment；
Fig. 4 is energy-storage system power distribution and nonparametric probability figure in embodiment.
Embodiment
Below in conjunction with the accompanying drawings, embodiment is elaborated.
The present invention proposes a kind of energy-storage system typical curve method for digging based on cloud model, as shown in figure 1, including：
Step 1, set power swing of the application scenarios of energy-storage system to stabilize photovoltaic plant.Setting time span is 1 Year.Selection control strategy is first-order low-pass ripple.As shown in Fig. 2 the light stores up association system mainly by photovoltaic plant, energy-storage system Formed with control system.PsFor photovoltaic plant power output, PbIt is energy-storage system power output, PwIt is that light storage association system is always defeated Go out power.Their existing relations are shown below (after discretization)：
Pout(k)=α Pout(k-1)+(1-α)·Ps(k)
Ps(k)+Pb(k)=Pout(k)
Wherein, k walks for time discretization, and the corresponding moment is tk=t0+ k Δs t, t0For initial time, Δ t is when sampling Between be spaced.τ is the time constant of low pass filter, and α is the constant relevant with τ, referred to as filter constant.
According to above-mentioned control strategy and equation, the energy-storage system charge and discharge power of 1 year is calculated.
Charge and discharge power to energy-storage system carries out data acquisition, and note jth day k moment energy-storage system charge and discharges performance number is Pjk, j =1,2 ..., n, n is count total number of days, and k=1,2 ..., s, s is daily sampled point number, and k moment charge and discharges power is maximum in n days It is worth for Pkmax, then perunit value of the energy-storage system charge and discharge power at the k moment be：Pjk_pu=Pjk/Pkmax, Pjk_pu∈[0,1]；
Step 2, the perunit charge and discharge performance number at different moments for counting n days are simultaneously stored in matrix Ms*n=[p1,p2,…,pk,…, ps]T, pkIt is the perunit charge and discharge vector power at k moment in energy-storage system n days, pk=[P1k_pu,P2k_pu,…,Pnk_pu]；
Step 3, statistics perunit charge and discharge vector power pkFrequency distribution, obtain pkCurve of frequency distribution f (x)k, and root According to curve of frequency distribution f (x)kPeak FkjCorresponding abscissa obtains the expectation E of cloud modelxki, i.e. the position of centre of gravity of cloud；i =1,2 ..., m, initial value i=1；
Step 4, the expectation E with cloud modelxkiCentered on, first trough position of the right and left appearance is found out, is divided X is not designated as itleftWith xright, it is determined that the water dust scope d of first cloud modeli=min (Exki-xleft,xright-Exki)；Utilize Reverse cloud algorithm without degree of certainty information calculates entropy ETkiWith super entropy Heki, then calculate cloud model data distribution function fcloud-ki；
Step 5, from curve of frequency distribution f (x)kIn subtract cloud model distribution function fcloud-ki, obtain new frequency distribution Curve f ' (x)k, and find f ' (x)kPeak Fki+1；Calculate peak FkiWith peak Fki+1Difference Distanceki=Fki- Fki+1；
If step 6, DistancekiMore than or equal to threshold epsilon, then peak F is found outki+1Corresponding abscissa Exki+1, then i=i+ 1 and 4~step 5 of repeat step, until DistancekmLess than threshold epsilon, and obtain corresponding abscissa Exki；If DistancekiLess than threshold epsilon, then k=k+1, that is, enter the normal cloud model group for finding subsequent time；
Step 7, the representative charge and discharge performance number vector at k moment is obtained according to step 4~step 6：(Exk1,Exk2,…, Exki,…,Exkm)；Energy-storage system is as follows in the computational methods of the typical charge and discharge performance number at k moment：
Step 8, k+1 is calculated successively, k+2 ..., energy-storage system typical case's charge and discharge performance number at s moment, obtain energy-storage system allusion quotation Type operation curve P=[P1, P2..., Ps]。
Reverse cloud algorithm in the step 4 is：
Step 401, by scope in (Exk1-d1, Exk1+d1) in power data be designated as xk1i, i=1,2 ..., T；According to sample This xk1iSample is calculated it is expected：
Step 402, calculate sample variance:
Step 403, calculate entropy:
Step 404, calculate super entropy:
The data distribution function computational methods of cloud model in the step 4 are：
Energy-storage system typical case's operation curve is excavated according to the algorithm proposed, as shown in Figure 3.Its charge and discharge power is united Meter with nonparametric probability be fitted, and with 6 kinds of typical weathers (be sunny, fine (thin cloud) by typical weather type set, it is fine Turn cloudy, cloudy turn to fine, cloudy, 6 kinds of overcast and rainy snow etc., and each typical weather type corresponds to a kind of photovoltaic power situation, each Kind photovoltaic power power situation corresponds to a kind of energy-storage system charge and discharge power features.) under energy-storage system charge-discharge electric power be distributed it is non- The matched curve of parameter Density Estimator contrasts, as shown in Figure 4.From fig. 4, it can be seen that the norm of nonparametric kernel density of typical operation curve Evaluation fitting curve is bonded preferably with energy storage charge and discharge power matched curve under 6 kinds of typical weathers.Typical case's fortune shown in this explanation Fig. 3 Row curve has the feature of energy storage charge and discharge power curve under 6 kinds of typical weathers concurrently, can characterize energy-storage system and stabilize photovoltaic plant Power feature under power swing application scenarios.
This embodiment is only the preferable embodiment of the present invention, but protection scope of the present invention is not limited thereto, Any one skilled in the art the invention discloses technical scope in, the change or replacement that can readily occur in, It should all be included within the scope of the present invention.Therefore, protection scope of the present invention should be with scope of the claims It is defined.

Claims (3)

  1. A kind of 1. energy-storage system typical curve method for digging based on cloud model, it is characterised in that including：
    Step 1, the application scenarios and control strategy for determining energy-storage system, the charge and discharge power to energy-storage system carry out data acquisition, Remember that jth day k moment energy-storage system charge and discharges performance number is Pjk, j=1,2 ..., n, to count total number of days, k=1,2 ..., s, s is n Daily sampled point number, k moment charge and discharge power maximum is P in n dayskmax, then perunit of the energy-storage system charge and discharge power at the k moment It is worth and is：Pjk_pu=Pjk/Pkmax, Pjk_pu∈[0,1]；
    Step 2, the perunit charge and discharge performance number at different moments for counting n days are simultaneously stored in matrix Ms*n=[p1,p2,…,pk,…,ps]T, pkIt is the perunit charge and discharge vector power at k moment in energy-storage system n days, pk=[P1k_pu,P2k_pu,…,Pnk_pu]；
    Step 3, statistics perunit charge and discharge vector power pkFrequency distribution, obtain pkCurve of frequency distribution f (x)k, and according to frequency Rate distribution curve f (x)kPeak FkiCorresponding abscissa obtains the expectation E of cloud modelxki, i.e. the position of centre of gravity of cloud；I=1, 2 ..., m, initial value i=1；
    Step 4, the expectation E with cloud modelxkiCentered on, first trough position of the right and left appearance is found out, is remembered respectively For xleftWith xright, it is determined that the water dust scope d of first cloud modeli=min (Exki-xleft,xright-Exki)；Using need not The reverse cloud algorithm of degree of certainty information calculates entropy ETkiWith super entropy Heki, then calculate cloud model data distribution function fcloud-ki；
    Step 5, from curve of frequency distribution f (x)kIn subtract cloud model distribution function fcloud-ki, obtain new curve of frequency distribution f’(x)k, and find f ' (x)kPeak Fk(i+1)；Calculate peak FkiWith peak Fk(i+1)Difference Distanceki=Fki- Fk(i+1)；
    If step 6, DistancekiMore than or equal to threshold epsilon, then peak F is found outk(i+1)Corresponding abscissa Exk(i+1), then i=i+1 And 4~step 5 of repeat step, until DistancekiLess than threshold epsilon, and obtain corresponding abscissa Exki；If DistancekiLess than threshold epsilon, then k=k+1, that is, enter the normal cloud model group for finding subsequent time；
    Step 7, the representative charge and discharge performance number vector at k moment is obtained according to step 4~step 6：(Exk1,Exk2,…,Exki,…, Exkm)；Energy-storage system is as follows in the computational methods of the typical charge and discharge performance number at k moment：
    <mrow> <msub> <mi>P</mi> <mi>k</mi> </msub> <mo>=</mo> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </munderover> <mfrac> <msub> <mi>F</mi> <mrow> <mi>k</mi> <mi>i</mi> </mrow> </msub> <mrow> <munderover> <mo>&amp;Sigma;</mo> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>m</mi> </munderover> <msub> <mi>F</mi> <mrow> <mi>k</mi> <mi>i</mi> </mrow> </msub> </mrow> </mfrac> <mo>&amp;CenterDot;</mo> <msub> <mi>E</mi> <mrow> <mi>x</mi> <mi>k</mi> <mi>i</mi> </mrow> </msub> </mrow>
    Step 8, k+1 is calculated successively, k+2 ..., energy-storage system typical case's charge and discharge performance number at s moment, obtain energy-storage system typical case's fortune Row curve P=[P1, P2..., Ps]。
  2. 2. method according to claim 1, it is characterised in that the reverse cloud algorithm in the step 4 is：
    Step 401, by scope in (Exki-d1, Exki+d1) in power data be designated as xk1i, i=1,2 ..., T；According to sample xk1i Sample is calculated it is expected：
    Step 402, calculate sample variance:
    Step 403, calculate entropy:
    Step 404, calculate super entropy:
  3. 3. method according to claim 2, it is characterised in that the data distribution function of the cloud model in the step 4 calculates Method is：
    <mrow> <msub> <mi>f</mi> <mrow> <mi>c</mi> <mi>l</mi> <mi>o</mi> <mi>u</mi> <mi>d</mi> <mo>-</mo> <mi>k</mi> <mi>i</mi> </mrow> </msub> <mo>=</mo> <mi>exp</mi> <mo>&amp;lsqb;</mo> <mo>-</mo> <mfrac> <msup> <mrow> <mo>(</mo> <msub> <mi>x</mi> <mrow> <mi>k</mi> <mi>l</mi> <mi>i</mi> </mrow> </msub> <mo>-</mo> <msub> <mi>E</mi> <mi>x</mi> </msub> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mrow> <mn>2</mn> <msubsup> <mi>E</mi> <mrow> <mi>T</mi> <mi>k</mi> <mi>i</mi> </mrow> <mn>2</mn> </msubsup> </mrow> </mfrac> <mo>&amp;rsqb;</mo> <mo>.</mo> </mrow> 2
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CN106779340B (en) * 2016-12-01 2023-08-15 中国电力科学研究院 Extraction method and evaluation system of typical working condition curve of energy storage system
CN107194495B (en) * 2017-04-21 2020-05-12 北京信息科技大学 Photovoltaic power longitudinal prediction method based on historical data mining
CN113991711B (en) * 2021-11-16 2023-06-16 国网甘肃省电力公司电力科学研究院 Capacity configuration method for energy storage system of photovoltaic power station

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