CN109787279B - Interval quantity solving method for uncertain scheduling problem of wind power grid-connected system - Google Patents

Interval quantity solving method for uncertain scheduling problem of wind power grid-connected system Download PDF

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CN109787279B
CN109787279B CN201910032261.4A CN201910032261A CN109787279B CN 109787279 B CN109787279 B CN 109787279B CN 201910032261 A CN201910032261 A CN 201910032261A CN 109787279 B CN109787279 B CN 109787279B
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程杉
杨堃
冯毅煁
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China Three Gorges University CTGU
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Abstract

A method for solving interval quantity of uncertain scheduling problems of a wind power grid-connected system comprises the following steps: the nonlinear characteristic of the wind speed time sequence and the random characteristic of wind power generation are considered, and the wind power output prediction result is expressed in intervals; establishing an objective function taking the lowest total power generation cost of the schedulable unit as an optimization target, and establishing an interval scheduling model based on the interval quantity, wherein the interval scheduling model comprises basic constraint conditions; establishing an auxiliary model, and solving upper and lower bound values of a solution of each decision variable interval; establishing an auxiliary model, and solving upper and lower bound values of an interval solution of an optimal value of an objective function; optimizing the interval scheduling result containing the wind power grid-connected system by using a scene reduction method; and (4) carrying out example analysis by considering the actual situation, and analyzing the relation between the scale of the interval quantity and the interval scheduling problem. The method provides a new idea for solving the uncertain scheduling problem of the wind power grid-connected system, is favorable for reducing the conservatism of interval scheduling results through scene reduction, and realizes better evaluation and prediction.

Description

Interval quantity solving method for uncertain scheduling problem of wind power grid-connected system
Technical Field
The invention relates to the technical field of uncertain scheduling of new energy grid connection, in particular to a method for solving interval quantity of uncertain scheduling problems of a wind power grid connection system.
Background
With the large-scale access of renewable energy to the power grid, the influence of uncertain factors needs to be fully considered in power system scheduling nowadays, and the practicability of a scheduling result is also influenced by the accuracy of uncertain quantity prediction. At present, a great deal of work is usually to generate a corresponding optimal scheduling scheme on an obtained prediction result after a prediction method is adopted to generate a prediction value of a variable. Because the prediction of the new energy output is often not accurate, a large deviation exists between the prediction result obtained by the method and an actual value, and the scheduling result generally does not accord with an actual optimization scheme. Based on the above thought, how to reduce the deviation is very important.
Disclosure of Invention
The invention provides a method for solving interval quantity of uncertain scheduling problems of a wind power grid-connected system, which considers the interval quantity as a prediction result of wind power, directly considers the error between a variable prediction value and an actual value into scheduling, and gives a final result in an interval form by using the interval quantity to deal with the randomness in the uncertain scheduling problems. The method can effectively consider the influence of uncertain factors, and can obtain the interval scheduling result more suitable for practical application, so that a scheduling party can better predict and evaluate behaviors.
The technical scheme adopted by the invention is as follows:
a method for solving interval quantity of uncertain scheduling problems of a wind power grid-connected system comprises the following steps:
step 1: the nonlinear characteristic of the wind speed time sequence and the random characteristic of wind power generation are considered, and the wind power output prediction result is expressed in intervals;
step 2: establishing an objective function taking the lowest total power generation cost of the schedulable unit as an optimization target, and establishing an interval scheduling model based on the interval quantity, wherein the interval scheduling model comprises basic constraint conditions;
and step 3: establishing an auxiliary model, and solving upper and lower bound values of a solution of each decision variable interval;
and 4, step 4: establishing an auxiliary model, and solving upper and lower bound values of an interval solution of an optimal value of an objective function;
and 5: optimizing the interval scheduling result containing the wind power grid-connected system by using a scene reduction method;
step 6: and (4) carrying out example analysis by considering the actual situation, and analyzing the relation between the scale of the interval quantity and the interval scheduling problem.
In step 1, a wind power output prediction result graph which expresses wind power output in intervals is established on a wind power prediction result based on point values by specifying a relative error limit.
In step 2, the lowest total power generation cost of the schedulable unit is taken as an optimization target, and the relationship between the power generation cost and the output power of one schedulable unit is in a quadratic function form, so that the target function of the wind power grid-connected system is as follows:
Figure BDA0001944659640000021
in the formula: a. then、Bn、CnAre all coefficients of a schedulable unit n power generation cost function, Pn hIs the output power of the nth schedulable unit, and h represents the number of time segments.
In step 2, the basic constraint conditions of the interval scheduling model include:
1) system power balance constraint:
Figure BDA0001944659640000022
in the formula: n and M are the number of the units participating in scheduling and the number of the wind power units respectively, and H is the total time interval number in the scheduling period; pL h、Pn h、Pml h、Pmu hThe load in the h time period, the output power of the nth schedulable unit and the upper and lower boundaries of the output interval of the mth wind turbine unit are respectively.
2) Output restraint of the schedulable unit:
Figure BDA0001944659640000023
in the formula: pn max、Pn minRespectively an upper limit and a lower limit of the allowable output of the schedulable unit n.
3) And the climbing rate of the schedulable unit is restrained:
Figure BDA0001944659640000024
in the formula: r isu n、rd nThe output of the schedulable unit n is increased and the climbing rate is reduced; t is60The response time is 60 min.
4) And rotating standby constraint:
positive rotation:
Figure BDA0001944659640000025
negative rotation:
Figure BDA0001944659640000026
in the formula: rn,u h/Rn,d hTo be adjustableThe positive/negative rotation reserve capacity of the unit n in the h period; l isu％/Ld% is the positive/negative prediction error limit for the load.
5) Output balance restraint of the schedulable unit:
Figure BDA0001944659640000031
in the formula: and epsilon is a power balance coefficient, is more than or equal to 0, and is higher when the value is smaller, and the balance degree among the output forces of different schedulable units is higher.
In step 2, the interval scheduling model is as follows:
Figure BDA0001944659640000032
in the formula: decision variable Pn hIs marked as P; the objective function is denoted as F (P); all the equality constraints are denoted as f, and the upper and lower limits of the equality constraints are denoted as fu、fl(ii) a All inequality constraints are normalized and recorded as g, and the upper and lower limits of inequality constraints are recorded as gu、gl。
In step 3, the established auxiliary model is as follows:
Figure BDA0001944659640000033
Figure BDA0001944659640000034
in the formula: pn hThe decision variable is the output power of the nth schedulable unit. f. ofu、flRespectively, the upper and lower limit values of the equality constraint, gu、glRespectively, the upper and lower limit values of the inequality constraint.
Wherein the result of the model solution is taken as Pnl hAnd Pnu hDecision variable Pn hSatisfy the constraint conditionValue range [ P ]nl h,Pnu h]。
In step 4, the established auxiliary model is as follows:
Figure BDA0001944659640000035
s.t.f(P)＝[fl,fu]
g(P)≤[gl,gu]
(11)
Figure BDA0001944659640000041
in the formula: a. then、Bn、CnAre all coefficients of a schedulable unit n power generation cost function, Pn hIs the output power of the nth schedulable unit. f. ofu、flRespectively, the upper and lower limit values of the equality constraint, gu、glRespectively, the upper and lower limit values of the inequality constraint.
The result of the model solution is respectively used as the lower bound and the upper bound of the optimal value of the objective function, and the corresponding decision variables Pn hThe value of (a) is used as the boundary value of the solution of each interval, and the output interval of each schedulable unit is obtained when the target function takes the optimal value.
In step 5, the scene reduction method is applied to the interval scheduling problem, and part of low-value scenes in the interval quantity are discarded in a proper amount.
The invention discloses a method for solving interval quantity of uncertain scheduling problems of a wind power grid-connected system, which has the following technical effects:
1: in step 5, a scene reduction method is adopted to reduce the conservative property of interval results. Because the interval result obtained by strict calculation is always conservative and the value range is overlarge, the scene reduction method is applied to the interval scheduling problem, and partial low-value scenes in the interval quantity are discarded in a proper amount. The idea of the scene reduction method adopted by the invention is as follows: and setting the value range of a single interval quantity corresponding to a certain variable, wherein the variable obeys a certain probability density function, and the probabilities corresponding to the subintervals reduced at the two ends of the specified interval quantity are equal. And accordingly the widths of the two sub-intervals are not necessarily equal. After the scene reduction method is used, the scheduling results before and after scene reduction can be simultaneously reserved, and both results have reference values.
2: the method provides a new idea for solving the uncertain scheduling problem of the wind power grid-connected system, and is beneficial to reducing the conservatism of interval scheduling results through scene reduction, so that better evaluation and prediction are realized.
Drawings
The invention is further illustrated by the following examples in conjunction with the accompanying drawings:
FIG. 1 is a graph of the prediction results of the output interval of 4 wind turbines.
Fig. 2 is a graph of the prediction of the load.
Fig. 3 is a diagram of an output power interval of the generator set.
FIG. 4 is a graph of the output power interval of each generator set when the total power generation cost is the lowest.
Fig. 5 is a graph of the output power intervals of the generator sets after the scene cut.
FIG. 6(1) is a graph of wind power output of type 1 randomly generated wind power;
FIG. 6(2) is a 2 nd randomly generated wind power output curve;
FIG. 6(3) is a graph of the wind power output of the type 3 random generation.
FIG. 7(1) is a power curve diagram of each generator set when the total electrical cost is the lowest under the condition of the 1 st wind power output;
FIG. 7(2) is a power curve diagram of each generator set when the total electrical cost is the lowest under the 2 nd wind power output condition;
fig. 7(3) is a power curve diagram of each generator set when the total electric cost is the lowest under the condition of the 3 rd wind power output.
Fig. 8 is an explanatory diagram of 4 wind power scales.
FIG. 9 is a wind power output interval diagram under 4 different wind power scales.
FIG. 10 is a graph of the effect of the size of the interval on the optimum value of the objective function.
Fig. 11(1) is a graph showing the influence of the scale of the interval on the reduction ratio.
Fig. 11(2) is a graph showing the influence of the scale of the interval amount on the reduction amount.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited to these examples.
The simulation analysis is carried out by taking 4 wind generating sets and 4 generating sets as examples, the basic parameters of the 4 generating sets are shown in table 1, and the generating cost coefficients of the generating sets are shown in table 2.
TABLE 14 basic parameters of the generator set
Figure BDA0001944659640000051
Table 24 generating set generating cost coefficient
A B C
Unit 1 0.00043 20.6 958.2
Unit 2 0.00053 22.05 1613.6
Unit 3 0.00039 20.81 604.97
Unit 4 0.0006 21.3 871.6
The output interval prediction results of the 4 wind turbine generators in each time period are shown in fig. 1. The predicted load curve is shown in fig. 2. The output power value interval obtained by solving the auxiliary model is shown in fig. 3; and the output power value interval when the power generation cost is the lowest is shown in fig. 4. The results obtained after applying the method of scene cuts are shown in fig. 5. The 3 randomly generated wind power output graphs and the power graphs of the generator sets with the lowest total electric cost are respectively shown in fig. 6(1), 6(2), 6(3), 7(1), 7(2) and 7 (3). 4 mathematical example explanatory diagrams and wind power output interval diagrams with different wind power scales are respectively shown in fig. 8 and fig. 9. The influence of the scale of the interval amount on the optimum value of the objective function is shown in fig. 10, and the influence of the scale of the interval amount on the reduction ratio and the reduction amount is shown in fig. 11(1) and 11(2), respectively.
1. Model of total generation cost of schedulable unit:
Figure BDA0001944659640000061
in the formula: a. then、Bn、CnAll are coefficients P of a schedulable unit n power generation cost functionn hIs the output power of the nth schedulable unit.
2. Constraint model of scheduling problem:
1) system power balance constraint:
Figure BDA0001944659640000062
in the formula: n and M are the number of the units participating in scheduling and the number of the wind power units respectively, H is the number of time periods in a scheduling period, and H is taken as 24; pL h、Pn h、Pml h/Pmu hThe load in the h time period, the output power of the nth schedulable unit and the upper/lower boundary of the output interval of the mth wind turbine unit are respectively.
2) The output constraint of the dispatchable unit is as follows:
Figure BDA0001944659640000063
in the formula: pn max、Pn minRespectively an upper limit and a lower limit of the allowable output of the schedulable unit n.
3) And (3) the climbing rate of the dispatchable unit is restrained:
Figure BDA0001944659640000064
in the formula: r isu n/rd nThe slope climbing rate for the schedulable unit n output increase/decrease; t is60The response time is 60 min.
4) Rotating standby constraint:
positive rotation:
Figure BDA0001944659640000065
negative rotation:
Figure BDA0001944659640000071
in the formula: rn,u h、Rn,d hThe reserve capacity of the adjustable unit n in positive and negative rotation in the h time period is set; l isu％、Ld% is the positive and negative prediction error limits of the load. Setting the positive and negative prediction error limits L of the loadu％、LdThe% are 10%.
5) The output balance constraint of the dispatchable unit is as follows:
Figure BDA0001944659640000072
in the formula: and epsilon is a power balance coefficient, is more than or equal to 0, and is higher when the smaller the value is, the higher the balance degree between the output forces of the corresponding different schedulable units is. And setting the power balance coefficient epsilon of the unit as 30.
3. An auxiliary model:
a first auxiliary model:
Figure BDA0001944659640000073
Figure BDA0001944659640000074
in the formula: pn hThe decision variable is the output power of the nth schedulable unit. f. ofu，flRespectively, the upper and lower limit values of the equality constraint, gu，glRespectively, the upper and lower limit values of the inequality constraint.
Since the known quantity in the uncertain scheduling problem model is the interval quantity, the solved decision variable is also the form of the interval quantity naturally. For each decision variable Pn hMust exist Pn hAny value in the interval can simultaneously enable f (P) and g (P) to meet the constraint condition. These intervals are set to be maximally expandable to [ P ]nl h,Pnu h]This is the optimal interval solution for each decision variable in scheduling. The results of the solutions (8) and (9) are respectively used as Pnl hAnd Pnu hTo obtain a decision variable Pn hSatisfy the requirement ofValue range [ P ] of constraint conditionnl h,Pnu h]Therefore, the output range of the unit during dispatching is predicted, and the output working condition of the unit is predicted.
And (2) auxiliary model II:
Figure BDA0001944659640000081
Figure BDA0001944659640000082
in the formula: a. then、Bn、CnAre all coefficients of a schedulable unit n power generation cost function, Pn hIs the output power of the nth schedulable unit. f. ofu，flRespectively, the upper and lower limit values of the equality constraint, gu，glRespectively, the upper and lower limit values of the inequality constraint.
The optimal value of the objective function is related to the values of the known interval quantities, and different optimal target values are generated under the condition that different values are obtained by the interval quantities in a combined manner, so that the optimal value of the objective function is also the interval quantity. The results of the solution of the models (10) and (11) are respectively used as the lower bound and the upper bound of the optimal value of the objective function, and the corresponding decision variables P are usedn hThe value of (a) is used as the boundary value of the solution of each interval, and the output interval of each unit when the target function takes the optimal value is obtained. When the (10) and the (11) are solved, the two-layer Min operation in the (10) can be equivalent to a single-layer Min operation; an alternative to solving the model (11) is proposed here: firstly, calculating the known interval quantity, for example, adding the interval quantities of the wind power in each time interval to obtain a single interval quantity representing the total wind power output in the time interval. A plurality of scenes { xi ] are extracted from the interval quantity at equal intervals with proper precision1,ξ2,…,ξkAnd k is the total number of the extracted scenes, then the optimal values of the objective function in each scene are respectively solved, and the approximate upper bound value of the optimal value of the objective function is obtained after comparison.
4. Scene reduction:
because the interval solution obtained by strict calculation is conservative, the range of the interval solution is too large, and the actual value of the variable is often far away from the boundary value of the interval quantity. The reason is that in practical problems, the probability of values of the variable at the boundary of the interval quantity and in the vicinity of the boundary is low, for example, if the predicted value of the wind power output power in a certain period obeys normal distribution, an output interval is generated through interval prediction, and it can be seen that the probability of values of the wind power at the boundary of the interval and in the vicinity of the interval is low. In combination with the above considerations, by adopting a scene reduction method to appropriately discard the part of scenes located at and near the boundaries at the two ends of the known interval quantity, the interval scheduling result more suitable for the actual demand can be obtained.
5. Analysis by calculation example:
1) and solving to obtain upper and lower bound values of each decision variable acquirable interval according to known parameters and a built mathematical model before considering the objective function, and obtaining output power value intervals of all schedulable units meeting the constraint condition as shown in figure 3. Because the output cost of the units is not considered, the output priority of each unit is the same, and the value intervals of the output of each unit in the same section are completely the same under the condition of not being constrained by the output of the units. In the periods 4, 13, 14 and 15, the upper bound of the output of the units 1 and 2 reaches the maximum value 250kW of the allowable output, while the upper bound of the output of the units 3 and 4 is not restricted by the output constraint of the units, so that the upper bound of the output power value intervals of the units at the same time period is not all the same. In addition, the power output range of the unit is influenced by the load and the wind power output condition, and the requirement on the unit power output in the 4 th time period is the highest in the period.
2) And the economic benefits of generating electricity by each unit are different in actual production. The output interval of all the units when the total power generation cost is the lowest is solved as shown in fig. 3. Comparing fig. 3, it can be verified that the output intervals of the units do not exceed the corresponding zone shown in fig. 3. The optimum value of the objective function, i.e. the minimum value of the lowest total cost of power generation is 4.7506 x 105Element with maximum value of 5.1513 × 105Yuan, the interval result is [4.7506,5.1513 ]]×105。
3) In the calculation example, the partial scenes of the predicted output intervals of the 4 wind turbines shown in fig. 1 are reduced, the scenes in the parts located at the two ends of the interval and 5% of the scenes in the parts located at the two ends of the interval are reduced, for example, the scenes of the interval quantity [0,100] are reduced to obtain [5,95], and the output intervals of the wind turbines are obtained through simulation operation and are shown in fig. 5. The intervals of output for each unit in fig. 5 are narrower than in fig. 4, and the intervals of output for each unit are substantially contained within their corresponding intervals in fig. 4.
4) And currently, respectively replacing the known interval quantity in the scheduling by the point value so as to more intuitively show the association between the interval result of the scheduling problem and the result of the conventional scheduling. And (3) taking a random number in the output interval of each time period of the wind turbine generator to replace the interval quantity, wherein the power curve of each wind turbine generator does not exceed the corresponding power interval zone in the graph 1. By randomly generating power curves of the wind turbines in the scheduling period, 3 wind power output conditions shown in fig. 6(1), 6(2) and 6(3) are obtained. After the known interval quantity in the original scheduling model is replaced by the point value, the power curve of the schedulable unit can be obtained by solving the model, and the power curves of the units under different conditions are shown in fig. 7(1), 7(2) and 7(3) corresponding to 3 wind power output conditions randomly generated in fig. 6(1), 6(2) and 6 (3). Fig. 7(1), 7(2) and 7(3) show that the power curves of all the units do not exceed the zone of the output power in fig. 4, and the lowest total power generation cost obtained by scheduling under the conditions 1 to 3 is 4.9662 x 105、4.9091×105、4.9429×105Elements whose values are also the result of scheduling in the previous interval [4.7506,5.1513 ]]×105Of the inner part of (a). These characteristics all show that the interval result of the scheduling has its unique meaning compared with the result of the conventional scheduling mode, namely, the range of the fluctuation of the scheduling result influenced by random factors is effectively known.
5) In order to further discuss the influence of the scale of the interval quantity on the interval scheduling result, the simulation is respectively carried out under 4 calculation examples shown in fig. 8, the wind power scales corresponding to each calculation example are different, the prediction interval of the output power of 6 wind turbine generators in each time interval is shown in fig. 9, wherein the output interval prediction results of the wind turbine generators 1-4 are still the same as those in fig. 1, and the load prediction curve in each calculation example is still shown in fig. 2. The interval solution for solving the optimal value of the target function under 4 arithmetic examples is shown in fig. 10. By observing the range of the interval solution, the larger the width of the interval solution in the calculation example with a larger wind power scale. This is because, after the scale of the interval quantity is increased, the influence of the superposition of the uncertain factors on the scheduling is also increased correspondingly, so that the width of the interval solution of the optimal value of the objective function is increased. With the increase of the number of the wind turbines, the interval zone is lower in the direction of the longitudinal axis, and the economic benefit brought by the power generation mode of green resources such as wind power and the like is proved.
6) Now, the influence of the scale of the interval on the effect of scene reduction is analyzed. For the output power prediction sections of different wind turbines, the reduction proportion is different, the scenes in the parts of 5%, 4.5%, 7%, 6%, 4% and 5.5% at the two ends of the output power prediction sections (fig. 9) of the wind turbines 1 to 6 are all reduced, and the influence of the scale of the obtained section quantity on the scene reduction effect is shown in fig. 11(1) and fig. 11 (2). When the number of the wind turbines is small, along with the change of the number of the wind turbines, the reduction proportion of the interval solution of the optimal value of the objective function is calculated and kept near a certain numerical value, the scale of the interval quantity has small influence on the reduction effect, the reduction effect is stable, and the usability of the scene reduction method in the interval scheduling problem is also shown to a certain degree. In addition, as the number of wind generating sets increases in the calculation, the reduction amount on the interval solution of the optimal value of the objective function also continuously increases because of the superposition of uncertain factors.

Claims (4)

1. A method for solving interval quantity of uncertain scheduling problems of a wind power grid-connected system is characterized by comprising the following steps:
step 1: the nonlinear characteristic of the wind speed time sequence and the random characteristic of wind power generation are considered, and the wind power output prediction result is expressed in intervals;
step 2: establishing an objective function taking the lowest total power generation cost of the schedulable unit as an optimization target, and establishing an interval scheduling model based on the interval quantity, wherein the interval scheduling model comprises basic constraint conditions;
the interval scheduling model is as follows:
Figure FDA0003570704910000011
in the formula:
Figure FDA0003570704910000012
the output power of the nth schedulable unit in the h time period is
Figure FDA0003570704910000013
As decision variables, denoted as P, the objective function is denoted as F (P); all the equality constraints are denoted as f, and the upper and lower limits of the equality constraints are denoted as fu、fl(ii) a All inequality constraints are normalized and recorded as g, and the upper and lower limits of inequality constraints are recorded as gu、gl；
And step 3: establishing an auxiliary model, and solving upper and lower bound values of a solution of each decision variable interval;
the auxiliary model established in the step 3 is as follows:
Figure FDA0003570704910000014
Figure FDA0003570704910000015
in the formula: f. ofu、flRespectively, the upper and lower limit values of the equality constraint, gu、glRespectively the upper and lower limit values of inequality constraint; n is the number of schedulable units, and H is the total time interval number in the scheduling period;
wherein the objective function
Figure FDA0003570704910000016
The result obtained as the result of the interval
Figure FDA0003570704910000017
A value of (d); objective function
Figure FDA0003570704910000018
The result obtained as the result of the interval
Figure FDA0003570704910000019
A value of (d); decision variables
Figure FDA00035707049100000110
The value interval satisfying the constraint condition is as follows:
Figure FDA00035707049100000111
and 4, step 4: establishing an auxiliary model, and solving upper and lower bound values of an interval solution of an optimal value of an objective function;
the auxiliary model established in the step 4 is as follows:
Figure FDA0003570704910000021
Figure FDA0003570704910000022
Figure FDA0003570704910000023
Figure FDA0003570704910000024
in the formula: a. then、Bn、CnAre all coefficients of a schedulable unit n power generation cost function, fu、flRespectively, the upper and lower limit values of the equality constraint, gu、glUpper and lower limits of respectively inequality constraintsA value;
the result of the model solution is respectively used as the lower bound and the upper bound of the optimal value of the objective function, and the corresponding decision variables are used
Figure FDA0003570704910000025
The value of (2) is used as the boundary value of the solution of each interval to obtain the output interval of each schedulable unit when the target function takes the optimal value;
and 5: optimizing the interval scheduling result containing the wind power grid-connected system by using a scene reduction method;
step 6: and (4) carrying out example analysis by considering the actual situation, and analyzing the relation between the scale of the interval quantity and the interval scheduling problem.
2. The interval quantity solving method for the uncertain scheduling problem of the wind power integration system according to claim 1, characterized by comprising the following steps: in step 1, a wind power output prediction result graph which expresses wind power output in intervals is established on a wind power prediction result based on point values by specifying a relative error limit.
3. The interval quantity solving method for the uncertain scheduling problem of the wind power integration system according to claim 1, characterized by comprising the following steps: in step 2, the lowest total power generation cost of the schedulable unit is taken as an optimization target, and the relationship between the power generation cost and the output power of one schedulable unit is in a quadratic function form, so that the target function of the wind power grid-connected system is as follows:
Figure FDA0003570704910000026
in the formula: a. then、Bn、CnAre all coefficients of a generating cost function of the dispatchable unit n,
Figure FDA0003570704910000027
is the output power of the nth schedulable unit in the h time period.
4. The interval quantity solving method for the uncertain scheduling problem of the wind power integration system according to claim 1, characterized by comprising the following steps: in step 2, the basic constraint conditions of the interval scheduling model include:
1) system power balance constraint:
Figure FDA0003570704910000028
h＝1,2,…,H (2)
in the formula: n and M are the number of the schedulable units and the wind generating units respectively, and H is the total time interval number in the scheduling period;
Figure FDA0003570704910000031
the load in the h time period, the output power of the nth schedulable unit and the upper and lower boundaries of the output interval of the mth wind turbine unit are respectively set;
2) output restraint of the schedulable unit:
Figure FDA0003570704910000032
n＝1,2,…,N；h＝1,2,…,H
in the formula:
Figure FDA0003570704910000033
respectively an upper limit and a lower limit of allowable output of the schedulable unit n;
3) and the climbing rate of the schedulable unit is restrained:
Figure FDA0003570704910000034
n＝1,2,…,N；h＝2,3,…,H (4)
in the formula:
Figure FDA0003570704910000035
respectively increasing and reducing the climbing rate of the schedulable unit n output; t is60The response time is 60 min;
4) and rotating standby constraint:
Figure FDA0003570704910000036
in the formula:
Figure FDA0003570704910000037
respectively setting the positive and negative rotation reserve capacities of the schedulable unit n in the h time period; l isu％、Ld% is the positive and negative prediction error limits of the load, respectively;
5) output balance restraint of the schedulable unit:
Figure FDA0003570704910000038
in the formula: and epsilon is a power balance coefficient, epsilon is more than or equal to 0, and the smaller the value of epsilon is set, the higher the balance degree among the output forces of the different schedulable units is.
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