CN111416353B - Standby configuration method for considering wind power continuous period fluctuation trend - Google Patents
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- 230000000630 rising effect Effects 0.000 claims description 11
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- 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
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- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
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Abstract
The invention discloses a standby configuration method for considering wind power continuous period fluctuation trend, which comprises the following steps: the method comprises the steps of (1) constructing a multi-period wind power output power fluctuation trend set; (2) calculating a wind power fluctuation trend flag function; (3) Judging whether the wind power in the current period is monotonously changed in a period of time, adjusting the monotonously changed wind power to the step six, and executing the step four if not; (4) Reconstructing a future continuous period wind power output power fluctuation trend set; (5) updating a wind power fluctuation trend flag function; and (6) performing configuration optimization on the spare capacity. The invention provides sufficient data basis for judging the wind power fluctuation trend, and the spare capacity is configured according to the wind power fluctuation trend, so that the economy of operation scheduling is provided on the basis of meeting the system safety requirement.
Description
Technical Field
The invention belongs to the field of power system dispatching, and particularly relates to a standby configuration method for considering wind power continuous period fluctuation trend.
Background
Because of strong uncertainty of wind power in space-time distribution, the increase of large-scale wind power grid connection brings serious hidden danger to safe and stable operation of a power system. In order to effectively cope with the challenges brought to the scheduling operation of the power system by the fluctuation of the wind power output, fully excavating the prediction information of wind power in multiple time periods and configuring the operation reserve based on the prediction information has become one of effective ways for coping with wind power uncertainty.
The method for predicting the wind power output is mainly used for predicting the point of the wind power output value based on the prior probability, however, due to the strong uncertainty of wind power, the prediction accuracy is difficult to ensure for single point prediction at fixed time. The prediction precision is effectively improved by the interval prediction and scene prediction technology based on the point prediction technology. The interval prediction combines the prior probability and the conditional probability to estimate the posterior probability, and model errors caused by the assumption of specific probability distribution are avoided by giving out the output interval. The scene prediction technology generates a large number of scenes by adopting a Monte-Kelol sampling simulation method, and takes a simulation result as a main basis for measuring and calculating wind power uncertainty probability.
However, the existing point prediction, output interval prediction or scene prediction of wind power output is focused on the depiction of wind power output of a single time section. The wind power output has strong fluctuation on a time scale, the output fluctuation of adjacent time periods has certain correlation, and the prediction precision can be obviously improved by considering the wind power output fluctuation trend of multiple time periods. At present, the prediction of the wind power fluctuation trend is mostly based on the prediction information of the current period and a plurality of future periods, the prediction accuracy of the future periods is reduced along with the increase of the prediction time, and the effect of the history prediction information on the whole fluctuation trend prediction is not fully considered.
Object of the Invention
The invention aims to comprehensively consider historical wind power prediction data, and wind power prediction information of the current moment and the future moment time period is used for evaluating the overall change trend of wind power output in a certain continuous time period. And carrying out standby configuration of the power system scheduling plan according to the fluctuation trend of the wind power continuous period.
Disclosure of Invention
In order to achieve the above object, the present invention provides a standby configuration method for taking the trend of fluctuation of wind power continuous period into account, comprising the following steps:
step S1: constructing a multi-period wind power output power fluctuation trend set based on the past period, the current period and the future period prediction information;
step S2: calculating a wind power fluctuation trend marking function based on the wind power fluctuation trend set constructed in the step S1;
step S3: judging whether the wind power in the current period is monotonously changed in a period of time based on the wind power fluctuation trend marking function calculated in the step S2, if so, turning to the step S6, otherwise, executing the step S4;
step S4: reconstructing a wind power output power fluctuation trend set of a future continuous period by taking the current period and the future period prediction information as main basis;
step S5: updating the wind power fluctuation trend flag function in the step S2 based on the wind power output power fluctuation trend set in the future continuous period in the step S4;
step S6: and reading the spare capacity configured based on the single time section, and performing configuration optimization on the spare capacity based on the wind power fluctuation trend flag functions of S2 and S5.
Drawings
FIG. 1 is a flow chart of a standby configuration method that accounts for wind power continuous period fluctuation trends.
Detailed Description
The standby configuration method for accounting for the fluctuation trend of the wind power continuous period according to the invention is described in detail below with reference to the accompanying drawings.
FIG. 1 is a flowchart of an alternate configuration for accounting for wind power continuous period fluctuation trends, comprising the steps of:
step one: constructing a multi-period wind power output power fluctuation trend set.
And selecting the current moment, 4 scheduling moments before, and constructing a multi-period wind power output power fluctuation trend set by wind power data of the 4 scheduling moments in the future, wherein the trend set is shown in a formula (1).
Ω t8 ＝[P t-4 ,P t-3 ,P t-2 ,P t-1 ,P t ,P t+1 ,P t+2 ,P t+3 ,P t+4 ] (1)
Omega in t8 For a total of 8 wind power output power fluctuation trend sets at adjacent moments, P t And the predicted value of the wind power at the moment t.
Step two: and calculating a wind power fluctuation trend sign function.
Calculating a wind power fluctuation trend flag function based on a wind power output power fluctuation trend set given by the formula (1):
in gamma t8 For a total of 8 adjacent moments of wind power fluctuation trend sign functions, sign (x) is a sign variable function, the function of which is as follows:
step three: judging whether the wind power in the current period is monotonously changed in a period of time, adjusting the monotonously changed to the step six, and executing the step four otherwise.
Specifically, according to the definition of the formula (3), the trend of fluctuation of wind power is different. Gamma ray t8 The value interval is [ -8, -6, -4, -2,0,2,4,6,8]. When |gamma t When |=8, at [ t-4, t+4]The fluctuation trend of wind power is single (continuously rising or continuously falling) in the time period. I.e. at time t, the wind power has a monotonically increasing or monotonically decreasing trend within a future period of time. When gamma is t8 At=8, the wind power rises monotonically in the future for a period of time. At this time, if negative errors occur in the wind power at the time t, when the continuous rising trend of the wind power needs to be satisfied, the negative errors at this time need to satisfy the formula (4):
P t-1 -P t <ξ t <0 (4)
in xi t And the wind power prediction error at the moment t is obtained.
Meanwhile, the wind power output at the moment t is ensured to present a continuous ascending trend in the following [ t+1, t+4] period on the basis of the maximum climbing capacity by the limit of the wind power climbing rate in the adjacent period, particularly the lower limit value of the wind power output deviation from the moment t to the moment t+1 when the wind power at the moment t needs to climb upwards is required to be met, as shown in a formula (5),
P t +ξ t +R um >P t+1 -ΔPD t+1 (5)
wherein R is um For the maximum upward climbing capacity of wind power at t moment, deltaPD t+1 And predicting the maximum negative deviation for the wind power at the time t+1.
If positive errors occur in wind power at the moment t, when the continuous rising trend of wind power needs to be met, the positive errors at the moment always need to meet the formulas (4) and (5), and only the positive deviation value plus the maximum climbing capacity of wind power output is required to be ensured to be smaller than the wind power output deviation upper limit at the moment t+1, as shown in the formula (6):
P t +ξ t +R um <P t+1 +ΔPU t+1 (6)
in DeltaPU t+1 And predicting the maximum positive deviation for the wind power at the time t+1.
When gamma is t8 When the wind power is in a monotonically rising state in a future period of time, the constraint received by the positive and negative deviation is compared and analyzed when the wind power is in the monotonic rising state in the future period of time. The maximum possible value of the negative deviation is low, subject to the double constraint of equations (4) and (5), and therefore the up-regulation to ensure safety requirements is low. While positive deviations are hardly subject to any restrictions except (6), so the reserved turndown reserve should be much larger than the turnup reserve.
And gamma is equal to t8 When γ is similar to =8 t8 When= -8, the wind power monotonically decreases in a future period of time. At this time, if the wind power is positively deviated at the time t, the constraint to be satisfied by the positive deviation is as shown in the formula (7) and the formula (8):
0<ξ t <P t-1 -P t (7)
P t +ξ t -R dm <P t+1 +ΔPU t+1 (8)
wherein R is dm And the maximum downward climbing capacity of the wind power at the moment t.
If the wind power generates negative deviation at the moment t, the constraint to be met by the negative deviation is shown as a formula (9):
P t +ξ t -R dm >P t+1 -ΔPD t+1 (9)
when gamma is t8 When the wind power is in a monotonically decreasing state in a future period of time in the time of the= -8, the constraint received by the positive and negative deviation is compared and analyzed. The maximum possible value of the positive deviation is low, subject to the double constraint of equations (7) and (8), and therefore the down-regulation to ensure safety requirements is low. While negative deviations are hardly subject to any restrictions except (9), so the reserved up-regulation reserve should be much larger than the down-regulation reserve.
In summary of the above analysis, when |γ t8 When =8, the original equal up-adjustment and down-adjustment should be adjusted to up-adjustment and down-adjustment with larger difference in values.
Taking into account |gamma t8 As the marker quantity for representing the monotonic change of the wind power fluctuation trend, the value of |gamma=8 is too severe t8 When |=6, at Ω t Only one adjacent moment does not meet the overall trend, and the overall trend is obvious in rising or falling trend, so that the total trend is in |gamma t8 When the value is =6, a low-value offset factor is added on the basis of the standby configuration method.
And when |gamma t8 |<And 6, at the moment, the wind power does not show obvious monotone trend in a specified period, and the standby configuration method cannot be adopted. Since the output of the historical period is finished, in order to further explore the sign of wind power in the future period, the prediction information of the future period is taken as a main basis, and the wind power output power fluctuation trend set in the continuous period is reconstructed.
Step four: and reconstructing a future continuous period wind power output power fluctuation trend set.
And selecting wind power data of the current moment and the future 4 scheduling moments to construct a multi-period wind power output power fluctuation trend set, wherein the trend set is shown in a formula (10).
Ω t4 ＝[P t ,P t+1 ,P t+2 ,P t+3 ,P t+4 ] (10)，
Omega in t4 For a total of 4 adjacent time instances of wind power output power fluctuation trend set.
Step five: and updating a wind power fluctuation trend flag function.
Updating a wind power fluctuation trend flag function based on the wind power output power fluctuation trend set reconstructed by the formula (10) is shown as a formula (11):
in gamma t4 The trend sign function of wind power fluctuation is used for totaling 4 adjacent moments.
Step six: and configuring and optimizing the spare capacity based on the wind power fluctuation trend flag function.
Specifically, if |γ t8 When the I is more than or equal to 6, judging that the change trend of the wind power in a period of time is monotonous in the third step, specifically, when gamma is t8 When not less than 6, the configured up-regulation R ur And downregulating R dr Satisfy formulas (12) to (14), respectively:
ω dr R dr ≥P t+1 +ΔPU t+1 -R um -P t (14)
wherein R is ur 、R dr And respectively carrying out up-regulation standby capacity and down-regulation standby for the system configured for coping with wind power prediction errors. Omega ur 、ω dr Fluctuating offset factor, ω, for up-regulation and down-regulation, respectively ur 、ω dr Are defined as shown in the formula (15) and the formula (16), respectively:
zhongχ (Chinese chi) ur 、χ dr R for implementing up-regulation respectively ur And downregulating R dr Is a transfer factor of (2);
when gamma is t8 And when the temperature is less than or equal to minus 6, the configured up-regulation reserve and the configured down-regulation reserve are respectively as follows:
ω ur R ur ≥P t -R dm -P t+1 +ΔPD t+1 (17)
omega in ur 、ω dr Are defined as shown in the formula (15) and the formula (16), respectively.
The constraints introduced above that need to be satisfied based on adjacent period wind power fluctuations are still applicable to |γ t8 |<6, i.e. the variables given in formula (11) in step five. However, with |gamma t8 When the I is more than or equal to 6, the monotonicity of the wind power fluctuation trend is obviously weakened, and the uncertainty of wind power can not be effectively treated when the wind power fluctuation trend is biased to be regulated in a single direction for standby. Thus, at that time, the standby configuration is more prone to up-and down-regulating standby being not so different.
When |gamma t8 |<6, the wind power fluctuation trend sign function is formed by gamma t8 Update to gamma t4 ；
When gamma is t4 =0, at this time, the wind power rises in half of the period, and falls in half of the period, and the wind power does not show a significant monotonic change trend in a future period. Thus, configuration standby should follow the principle that up-regulation standby and down-regulation standby are equal.
The total standby quantity required by the equipment is R sum Then
R ur ＝R dr ＝0.5*R sum (20)
Along the line of formula (20), when 0<|γ t When the level is less than or equal to 4, the wind power output still has a trend of obvious rising period or obvious falling period in the future period, and meanwhile, the monotone trend of the wind power output is not obvious enough compared with the situation, so that the adjustment for up-regulation or down-regulation is needed on the basis of the formula (20).
When gamma is t4 When not equal to 0, up-regulate R ur And downregulating R dr Satisfy the formulas (21) and (22) respectively,
R ur ＝(0.5-Δω ur )*R sum (21)
R dr ＝(0.5+Δω dr )*R sum (22)
wherein R is sum To the required standby total, Δω ur 、Δω dr Respectively for up-regulation ur And downregulating R dr Is defined as shown in formulas (23) - (24):
in χ ur 、χ dr R for implementing up-regulation respectively ur And downregulating R dr By setting χ ur 、χ dr The dynamic adjustment of the reserved reserve degree of the system which changes monotonically with the wind wave motion can be ensured.
Advantageous effects
Based on massive historical data information, the prediction forces of the historical period, the current period and the future period are comprehensively considered, and a sufficient data basis is provided for judging the wind power fluctuation trend. And the standby capacity is configured according to the wind power fluctuation trend, so that the economy of operation scheduling is provided on the basis of meeting the system safety requirement.
Claims (4)
1. A standby configuration method for considering wind power continuous period fluctuation trend is characterized by comprising the following steps:
step S1: constructing a multi-period wind power output power fluctuation trend set based on the past period, the current period and the future period prediction information;
step S2: calculating a wind power fluctuation trend marking function based on the wind power fluctuation trend set constructed in the step S1;
step S3: judging whether the wind power in the current period is monotonously changed in a period of time based on the wind power fluctuation trend marking function calculated in the step S2, if so, turning to the step S6, otherwise, executing the step S4;
step S4: reconstructing a wind power output power fluctuation trend set of a future continuous period by taking the current period and the future period prediction information as main basis;
step S5: updating the wind power fluctuation trend flag function in the step S2 based on the wind power output power fluctuation trend set in the future continuous period in the step S4;
step S6: reading the spare capacity configured based on a single time section, and performing configuration optimization on the spare capacity based on the wind power fluctuation trend flag functions of S2 and S5;
step S1 further comprises:
selecting current time, 4 scheduling time before, and constructing a multi-period wind power output power fluctuation trend set from wind power data of 4 scheduling time in the future, wherein the trend set is shown in a formula (1):
Ω t8 ＝[P t-4 ,P t-3 ,P t-2 ,P t-1 ,P t ,P t+1 ,P t+2 ,P t+3 ,P t+4 ] (1)，
omega in t8 For a total of 8 wind power output power fluctuation trend sets at adjacent moments, P t The wind power predicted value at the moment t;
step S2 further comprises:
based on the wind power output power fluctuation trend set given by the formula (1), calculating a wind power fluctuation trend flag function, as shown in the formula (2):
in gamma t8 For a wind power fluctuation trend flag function for a total of 8 adjacent moments, sign (x) is a flag variable function, and the function formula (3) thereof is as follows:
step S3 further comprises:
in the formula (2), gamma t8 The value interval is [ -8, -6, -4, -2,0,2,4,6,8]When |gamma t8 When |=8, at [ t-4, t+4]The fluctuation trend of the wind power is single in the time period, namely, at the time t, the wind power shows a monotonically rising or monotonically falling trend in a future period;
when gamma is t8 When=8, the wind power rises monotonically in a future period of time, at this time, if negative error occurs in wind power at time t, the continuous wind power rising trend needs to be satisfied, and at this time, the negative error needs to satisfy formula (4):
P t-1 -P t ＜ξ t ＜0 (4)，
in xi t For the wind power prediction error at the moment t, the wind power output at the moment t is ensured to be follow-up [ t+1, t+4] on the basis of the maximum climbing capacity]The wind power in the period shows continuous ascending trend, and the lower limit value of the wind power output deviation from the ascending slope of the wind power at the moment t to the moment t+1 needs to be met, as shown in the formula (5):
P t +ξ t +R um ＞P t+1 -ΔPD t+1 (5)，
wherein R is um For the maximum upward climbing capacity of wind power at t moment, deltaPD t+1 Predicting the maximum negative deviation for the wind power at the time t+1;
if positive errors occur in wind power at the moment t, the continuous rising trend of the wind power needs to be met, and the positive errors constantly meet the formulas (4) and (5), and the maximum ascending slope climbing capacity of the positive deviation value plus the wind power output is smaller than the upper limit of the wind power output deviation at the moment t+1, as shown in the formula (6):
P t +ξ t +R um ＜P t+1 +ΔPU t+1 (6)，
in DeltaPU t+1 Maximum positive deviation is predicted for the wind power at the time t+1;
when gamma is t8 When the wind power is in the range of = -8, the wind power is monotonically reduced in a future period, and if the wind power is in positive deviation at the moment t, the constraint to be satisfied by the positive deviation is as shown in the formula (7) and the formula (8):
0＜ξ t ＜P t-1 -P t (7)，
P t +ξ t -R dm ＜P t+1 +ΔPU t+1 (8)，
wherein R is dm For the maximum downward climbing capacity of the wind power at the moment t, if the wind power at the moment t generates negative deviation, the constraint to be met by the negative deviation is shown as a formula (9):
P t +ξ t -R dm ＞P t+1 -ΔPD t+1 (9)，
when |gamma t8 When |=6, Ω t Only one adjacent moment does not meet the overall trend, and the overall trend is obvious in rising or falling;
when |gamma t8 And when the I is less than 6, reconstructing a wind power output power fluctuation trend set in a continuous period by taking the prediction information of a future period as a basis.
2. A standby configuration method according to claim 1, wherein step S4 further comprises:
selecting wind power data of the current moment and 4 scheduling moments in the future to construct a multi-period wind power output power fluctuation trend set, wherein the trend set is shown in a formula (10):
Ω t4 ＝[P t ,P t+1 ,P t+2 ,P t+3 ,P t+4 ] (10)，
omega in t4 For a total of 4 adjacent time instances of wind power output power fluctuation trend set.
3. A standby configuration method according to claim 2, wherein step S5 further comprises:
based on the wind power output power fluctuation trend set reconstructed by the formula (10), updating a wind power fluctuation trend flag function as shown in the formula (11):
in gamma t4 The trend sign function of wind power fluctuation is used for totaling 4 adjacent moments.
4. A standby configuration method according to claim 3, wherein step S6 further comprises:
if |gamma t8 When the I is not less than 6, judging in the step S3 that the change trend of the wind power in a period of time is monotonous change, specifically, when gamma is t8 When not less than 6, the configured up-regulation R ur And downregulating R dr Satisfy formulas (12) to (14), respectively:
ω dr R dr ≥P t+1 +ΔPU t+1 -R um -P t (14)，
wherein R is ur 、R dr Up-regulation and down-regulation for the system to cope with wind power prediction error configuration respectively, omega ur 、ω dr Respectively is up-regulatingBy R ur And downregulating R dr Fluctuation shift factor, omega ur 、ω dr Are defined as shown in the formula (15) and the formula (16), respectively:
zhongχ (Chinese chi) ur 、χ dr R for implementing up-regulation respectively ur And downregulating R dr Is a transfer factor of (2);
when gamma is t8 At less than or equal to-6, configured up-regulation R ur And downregulating R dr Satisfy formulas (17) - (19), respectively:
ω ur R ur ≥P t -R dm -P t+1 +ΔPD t+1 (17)，
omega in ur 、ω dr Are defined as shown in formula (15) and formula (16), respectively;
when |gamma t8 When the I is less than 6, the wind power fluctuation trend sign function is formed by gamma t8 Update to gamma t4 ；
When gamma is t4 When=0, configured up-regulation R ur Equal to configured downregulation R dr The total required standby amount is R sum It satisfies the formula (20),
R ur ＝R dr ＝0.5*R sum (20)；
when gamma is t4 If not equal to 0, up-regulating preparationBy R ur And downregulating R dr Satisfy the formulas (21) and (22) respectively,
R ur ＝(0.5-Δω ur )*R sum (21)，
R dr ＝(0.5+Δω dr )*R sum (22)，
wherein R is sum To the required standby total, Δω ur 、Δω dr Respectively for up-regulation ur And downregulating R dr Is defined as shown in formulas (23) - (24):
in χ ur 、χ dr R for implementing up-regulation respectively ur And downregulating R dr By setting χ ur 、χ dr The reserved reserve of the system can be dynamically adjusted to monotonically change degree along with the wind wave motion.
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