CN112053034A - Power grid adjustable robust optimization scheduling method considering wind power uncertainty distribution characteristics - Google Patents

Power grid adjustable robust optimization scheduling method considering wind power uncertainty distribution characteristics Download PDF

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CN112053034A
CN112053034A CN202010799184.8A CN202010799184A CN112053034A CN 112053034 A CN112053034 A CN 112053034A CN 202010799184 A CN202010799184 A CN 202010799184A CN 112053034 A CN112053034 A CN 112053034A
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李玲芳
甘霖
李文云
朱涛
朱欣春
游广增
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Abstract

The invention relates to a power grid adjustable robust optimal scheduling method considering wind power uncertainty distribution characteristics. Aiming at the problem of uncertainty of power output caused by the fact that wind power is connected into a power grid, the invention firstly constructs a novel uncertain set of wind power output based on wind power fluctuation quantity distribution characteristics so as to realize accurate description of the robustness of a scheduling scheme. And secondly, aiming at the contradiction between the robustness optimization reliability and the economy, based on the constructed uncertain set, a method for calculating the penalty cost and the uncertain budget cost by introducing the robustness of the uncertain set is provided, so that the coordinated optimization of the robustness and the economy is realized. The method can effectively reduce the system operation cost, more reasonably arrange the unit operation plan, and the obtained optimized scheduling result has better robustness and economy and is easy to popularize and apply.

Description

Power grid adjustable robust optimization scheduling method considering wind power uncertainty distribution characteristics
Technical Field
The invention belongs to the technical field of wind power-containing power grid dispatching control, and particularly relates to a power grid adjustable robust optimal dispatching method considering wind power uncertainty distribution characteristics.
Background
Wind power, as an environment-friendly renewable energy source, has the advantages of low pollution, sustainability, no energy consumption and the like, and therefore, the wind power plays an important role in the development and utilization of new energy. However, wind power has poor controllability, the grid connection of the wind power brings great uncertainty to the operation of a power system, the safe operation and the effective wind power consumption of the system are difficult to ensure by adopting a traditional deterministic scheduling method, and the defects that uncertain variables are difficult to accurately depict, the calculated amount is large, the reliability cannot be ensured and the like exist by adopting methods such as random planning, opportunity constrained planning and the like based on probability theory. The robust optimization method well solves the problems, the uncertain variables are described in a set form, and the optimization solution can meet system constraints when the uncertain variables are randomly changed in the set.
One of the key problems of robust optimization is the depiction of an uncertain set, the traditional robust optimization method often defines that uncertain variables are uniformly distributed in an uncertain interval, the method lacks description of distribution characteristics of the uncertain variables, and the calculation result may greatly deviate from the actual result. In addition, the robust optimization method is based on the premise that the power grid can still meet the constraint of the power system under the worst condition, and therefore the optimization result is always conservative. Researchers provide an adjustable robustness optimization method for an uncertain interval, and the adjustable robustness parameters are introduced to achieve scaling of an uncertain set, so that the conservatism of a scheduling scheme is effectively reduced. However, most of the current researches only provide a method for reducing the conservatism of the scheduling scheme through a robustness parameter, and how to realize the cooperative optimization of the robustness and the objective function is not considered, so that the optimization effect is difficult to control. Therefore, the cooperative optimization of robustness and economy is realized, the optimization effect of the scheduling scheme is enhanced, the economy of system operation is improved, the wind power consumption capability of the system is enhanced, and great help is generated.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a power grid adjustable robust optimal scheduling method considering wind power uncertainty distribution characteristics. Firstly, a novel wind power output uncertain set based on wind power fluctuation quantity distribution characteristics is constructed, so that accurate description of wind power uncertainty is realized; and based on the established uncertain set, a penalty cost for introducing the robustness of the uncertain set and a calculation method for the uncertain budget cost are provided, so that coordinated optimization of robustness and economy is realized. The method can effectively improve the economical efficiency of system operation, enhance the wind power consumption capability of the system, and solve the influence of high-permeability renewable energy source access on the power grid.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a power grid adjustable robust optimization scheduling method considering wind power uncertainty distribution characteristics comprises the following steps:
step (1), constructing a novel wind power output uncertain interval based on wind power fluctuation quantity distribution characteristics; the establishment of the wind power output uncertain interval comprises two types: in short-term scheduling, an uncertain interval of wind turbine generator output is constructed through a wind speed predicted value, and the actual wind speed v at the next moment is constructed based on a wind speed predicted errorj,tA normal distribution probability density function; in ultra-short term scheduling, based onAn uncertain interval of wind turbine generator output is constructed according to wind speed and wind speed fluctuation at the current moment, and actual wind speed v at the next moment is constructed according to probability distribution of wind speed fluctuation quantity obtained through statistical fittingj,tA probability density function of;
step (2), constructing a power grid dispatching model for realizing coordinated optimization of robustness and economy based on the constructed uncertain interval; the objective function of the power grid dispatching optimization model is that the sum of the total power generation cost of the system, the rotating standby setting cost of the system and the wind and load abandoning punishment cost is minimized; and then, based on the calculation of the punishment cost and the uncertain budget cost of the robustness, solving an objective function to obtain the optimal output combination of the conventional generator set, the energy storage device and the external power grid which minimizes the total operation cost of the system and the reserved up-regulation and down-regulation rotary reserve of each generator set, and then scheduling according to the solving result.
Further, it is preferable that the specific steps of step (1) include:
in short-term scheduling, the relationship between the output power of the wind turbine generator and the wind speed is shown as formula (1):
Figure BDA0002628371040000021
in the formula:
Figure BDA0002628371040000022
is the output power of fan j at time t,
Figure BDA0002628371040000023
the unit installed capacity, v, of fan jj,tThe wind speed at the time t of the position of the fan j,
Figure BDA0002628371040000024
and
Figure BDA0002628371040000025
respectively cut-in and cut-out wind speed, v, of fan jj,ratedThe rated wind speed of the fan j;
the wind speed actual value is the sum of the wind speed predicted value and the wind speed prediction error, and is shown in the formula (2);
Figure BDA0002628371040000026
in the formula:
Figure BDA0002628371040000027
is a predicted value of wind speed and is a known quantity;
Figure BDA0002628371040000028
predicting error for wind speed, which is a random quantity;
Figure BDA0002628371040000029
upper and lower limits of wind speed prediction error;
the wind speed prediction error is regarded as a normal distribution random variable with a mean value of 0 and a standard deviation of sigma, and the probability density function of the wind speed actual value obtained by the formula (2) is as follows:
Figure BDA0002628371040000031
in the formula:
Figure BDA0002628371040000032
the wind speed of the fan j at the time t is predicted, and the standard deviation of the wind speed prediction deviation is taken as tau percent of the predicted value, namely
Figure BDA0002628371040000033
In the ultra-short-term scheduling, if the wind speed sequence of the position of the fan j in the T time period is Vj，Vj＝[vj,1,vj,2,...,vj,t-1,vj,t,...,vj,T]Defining the wind speed at the time t +1 as the wind speed at the time t and the wind speed fluctuation delta vjAnd (3) the sum:
vj,t+1＝vj,t+Δvj,t (4)
assuming that the probability density function of the fluctuation amount of the wind speed is h (Δ v)j) Therefore, the probability density function of the wind speed at the time t +1 is shown by the formula (5);
Figure BDA0002628371040000034
in the formula (I), the compound is shown in the specification,
Figure BDA0002628371040000035
the probability density function of the wind speed at the t +1 moment at the j position of the wind turbine generator; v. ofj，tIs the wind speed v of the position of the fan j at the moment tj,t+1Is the wind speed v of the position of the fan j at the moment t +1jmaxIs the maximum wind speed.
Further, it is preferable that the specific steps of step (2) include:
scaling the wind power output uncertain interval by using the robustness, wherein the value range is [0,1 ]; based on the value of the robustness, the upper and lower bounds of the scaled wind power output uncertain interval can be calculated according to the formulas (6) - (9); in short-term scheduling, calculating the upper and lower bounds of the wind power output uncertain interval according to the formulas (6) - (7), and in ultra-short-term scheduling, calculating the upper and lower bounds of the wind power output uncertain interval according to the formulas (8) - (9);
1) wind power uncertain interval based on wind speed fluctuation amount: when the wind speed is 0, the uncertain interval of the wind speed is an empty set, namely the wind speed is considered to have no fluctuation, and the wind speed at the moment t +1 is the same as the wind speed at the moment t;
Figure BDA0002628371040000041
Figure BDA0002628371040000042
Figure BDA0002628371040000043
Figure BDA0002628371040000044
in the formula:
Figure BDA0002628371040000045
the confidence interval of the wind speed at the t +1 moment under the robustness is obtained; v. ofj,tIs the wind speed v of the position of the fan j at the moment tj,t+1The wind speed of the position of the fan j at the moment t +1 is obtained;
Figure BDA0002628371040000046
as a predicted value of wind speed, vjmaxIs the maximum value of wind speed;
the objective function of the scheduling model is the minimum sum of the total power generation cost of the power system, the system rotation standby setting cost and the wind curtailment load curtailment cost, and the objective function is shown in formulas (10) to (11);
Figure BDA0002628371040000047
Figure BDA0002628371040000048
in the formula: c is the total operating cost of the system; cpThe total power generation cost of the system; cabandonThe total penalty cost of the system; creSetting cost for the system total rotation standby, namely the total uncertain budget cost; t is the total time period number of the scheduling; n is a conventional generator set;
Figure BDA0002628371040000049
active power is output for a conventional unit (such as a thermal power unit) i at the moment t,
Figure BDA0002628371040000051
for the kth energy storage device exchanging power with the power system at time t, Pt outFor exchanging power, P, between the electric power system and the external network at time twaste,t、Pcut,tRespectively, the wind abandoning power and the load abandoning power P of the system at the time tre,tThe system rotation reserve capacity is set for the scheduling scheme at the time t; f. ofpg(Pi,t) For the cost of electricity generation of conventional units, ai、bi、ciThe fuel cost coefficient of a conventional unit i; f. ofpo(Pt out) Exchanging power costs, κ, for an electric power system with an external gridPOIs a unit exchange power cost coefficient;
Figure BDA0002628371040000052
for the charging and discharging costs of the energy storage device, kappaesIs a charge-discharge cost coefficient; f. ofwaste(Pwaste,t)、fcut(Pcut,t) Punishment cost, kappa, for wind abandon and load abandon respectivelywaste,t、κcut,tIs a penalty cost coefficient; f. ofre(Pre,t) Uncertainty of budget cost, κ, for power systemcut,tA cost factor for spinning reserve;
solving the objective function to obtain the optimal output combination of the conventional generator set, the energy storage device and the external power grid which minimize the total operation cost of the power system and the reserved up-regulation and down-regulation rotation reserve of each conventional generator set
Figure BDA0002628371040000058
Reserved up-regulation and down-regulation rotation standby P for external power gridt outu、Pt outdAnd then scheduling according to the solving result.
Further, it is preferable that the constraints of the power grid dispatching model are as follows:
1) power balance constraint
Figure BDA0002628371040000053
In the formula: pL,tM, K are respectively a set of a wind turbine generator and an energy storage device for the load power at the moment t;
2) generator set output restraint
Figure BDA0002628371040000054
In the formula:
Figure BDA0002628371040000059
respectively representing the upper limit and the lower limit of the output of a conventional generator set i;
3) ramp rate constraint of generator set
Figure BDA0002628371040000055
In the formula:
Figure BDA0002628371040000056
respectively the maximum rising rate and the maximum falling rate of a conventional generator set i, and delta T is a scheduling time interval;
4) rotational standby restraint for generator set
Figure BDA0002628371040000057
Figure BDA0002628371040000061
In the formula:
Figure BDA0002628371040000062
up-turn reserve and down-turn reserve, P, respectively, for a conventional generator set iimaxIs the upper limit of i output, P, of a conventional generator setiminI, setting the lower limit of output for the conventional generator set;
5) external power grid and system exchange power constraint
Figure BDA0002628371040000063
In the formula:
Figure BDA0002628371040000064
respectively carrying out maximum and minimum values of power exchange between the power system and an external power grid;
6) external power grid providing rotating standby constraint
Figure BDA0002628371040000065
In the formula: pt outu、Pt outdThe up-regulation rotation standby and the down-regulation rotation standby are respectively provided for an external power grid;
7) output constraint accounting for spinning reserve
Figure BDA0002628371040000066
8) Energy storage device charge-discharge restraint
Figure BDA0002628371040000067
In the formula:
Figure BDA0002628371040000068
maximum charging and discharging power of the energy storage device k, respectively;
9) energy storage device state of charge constraint
Figure BDA0002628371040000069
In the formula: sk.tIs the state of charge of the energy storage device k at time t, Skmin、SkmaxRespectively, the upper limit and the lower limit of the state of charge of the energy storage device k.
Further, it is preferable that, when the robustness is less than 1, the curtailment wind-cut load power of the power system is calculated as shown in equations (22) to (24);
Figure BDA00026283710400000610
Figure BDA00026283710400000611
Figure BDA0002628371040000071
in the formula:
Figure BDA00026283710400000713
is a probability density function of the wind turbine j output at the time t obtained by the formulas (3) and (5), Pjcut,t、Pjwaste,tThe power of the wind abandon and the load abandon of the fan j at the time t,
Figure BDA00026283710400000714
the upper limit and the lower limit of the power output confidence interval of the fan j at the moment t, and u (x) is a step function;
in the solution, the formula (12) is rewritten into the forms of the formula (25) and the formula (26);
Figure BDA0002628371040000072
Figure BDA0002628371040000073
in the formula:
Figure BDA0002628371040000074
the conventional unit i outputs active power at the time t,
Figure BDA0002628371040000075
for the predicted output power of the wind turbine j at time t,
Figure BDA0002628371040000076
the actual output power of the wind turbine j at the moment t-1,
Figure BDA0002628371040000077
for the kth energy storage device exchanging power with the power system at time t, Pt outFor exchanging power, P, between the electric power system and the external network at time tL,tIs the load power at time t; n is a conventional generator set, and M, K is a set of a wind turbine generator set and an energy storage device respectively;
the objective function is rewritten as shown in equation (27):
Figure BDA0002628371040000078
the upper and lower output bounds of each unit should satisfy the formula (28):
Figure BDA0002628371040000079
in the formula:
Figure BDA00026283710400000710
P t outrespectively representing the upper and lower limits of the output of each controllable device;
Figure BDA00026283710400000711
the upper and lower boundaries of the wind turbine generator in the confidence interval are defined;
the up-regulation and down-regulation rotation standby of a conventional generator set, and the up-regulation and down-regulation rotation standby calculation formulas provided by an external power grid to a system are shown as formulas (29) and (30);
Figure BDA00026283710400000712
Figure BDA0002628371040000081
in the formula:
Figure BDA0002628371040000082
is to solve for min { CpThe output of a conventional unit and the system exchange power in the obtained system scheduling strategy;
and solving the objective function to obtain the optimal output combination of the conventional generator set, the energy storage device and the external power grid which minimize the total operation cost of the system and the reserved up-regulation and down-regulation rotary standby of each generator set, and then scheduling according to the solving result.
Further, it is preferable to solve by YALMIP.
The invention also discloses a power grid adjustable robust optimization scheduling system considering wind power uncertainty distribution characteristics, which comprises:
the first processing module is used for constructing a novel wind power output uncertain interval based on wind power fluctuation quantity distribution characteristics; the establishment of the wind power output uncertain interval comprises two types: in short-term scheduling, an uncertain interval of wind turbine generator output is constructed through a wind speed predicted value, and the actual wind speed v at the next moment is constructed based on a wind speed predicted errorj,tA normal distribution probability density function; in ultra-short-term scheduling, an uncertain interval of wind turbine generator output is constructed based on current-time wind speed and wind speed fluctuation, and the actual wind speed v at the next moment is constructed according to the probability distribution of the wind speed fluctuation quantity obtained by statistical fittingj,tA probability density function of;
the second processing module is used for constructing a power grid dispatching model for realizing coordinated optimization of robustness and economy based on the constructed uncertain interval; the objective function of the power grid dispatching optimization model is that the sum of the total power generation cost of the system, the rotating standby setting cost of the system and the wind and load abandoning punishment cost is minimized;
and the scheduling module is used for calculating the punishment cost and the uncertain budget cost based on the robustness, obtaining the optimal output combination of the conventional generator set, the energy storage device and the external power grid which minimizes the total operation cost of the system and the reserved up-regulation and down-regulation rotary standby of each generator set by solving an objective function, and then scheduling according to the solving result.
The invention also discloses electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can be run on the processor, wherein the processor realizes the steps of the power grid adjustable robust optimization scheduling method considering the wind power uncertainty distribution characteristics when executing the program.
The invention further discloses a non-transitory computer-readable storage medium on which a computer program is stored which, when being executed by a processor, carries out the steps of the grid adjustable robust optimized scheduling method as described above taking into account wind power uncertainty distribution characteristics.
The invention relates to a power grid adjustable robust optimization scheduling method considering wind power uncertainty distribution characteristics.
The meaning of the wind power output uncertain interval is the possible output range of wind power at the next moment, and the invention provides two construction methods of the wind power output uncertain interval. In short-term scheduling, an uncertain interval of wind turbine generator output is constructed through a wind speed predicted value, and an actual wind speed v at the next moment is constructed based on a wind speed predicted errorj,tA normal distribution probability density function; in ultra-short-term scheduling, an uncertain interval of wind turbine generator output is constructed through wind speed and wind speed fluctuation at the current moment, and the actual wind speed v at the next moment is constructed based on the probability distribution of the wind speed fluctuation quantity obtained through statistical fittingj,tA probability density function of;
based on the wind power uncertain interval of the wind speed fluctuation amount, the electric power system can stabilize the wind power fluctuation in the wind power confidence interval obtained by calculating the robustness, and the wind power fluctuation can not be stabilized outside the confidence interval and the uncertain interval. The maximum value of the robustness is 1, and the curtailment wind-cutting load power is 0 when the robustness is 1.
The present invention does not specifically limit the conventional power plant, such as a thermal power plant.
Compared with the prior art, the invention has the beneficial effects that:
the invention constructs a novel wind power output uncertain set based on wind power fluctuation quantity distribution characteristics, thereby realizing accurate description of wind power uncertainty; and based on the provided uncertain set, the robustness is introduced into the economic target of the optimization model, and the cooperative optimization of the robustness and the economy is realized. The invention can solve the robustness which can minimize the total system cost, thereby effectively reducing the system operation cost, more reasonably arranging the unit operation plan, and obtaining the optimized scheduling result which has better robustness and economy.
Drawings
FIG. 1 is a schematic diagram of an uncertain interval construction mode;
FIG. 2 is a graph of fan output power versus wind speed;
FIG. 3 is a schematic structural diagram of a wind speed confidence interval;
FIG. 4 shows the predicted wind speed and the actual wind speed of the day;
FIG. 5 shows the calculation results of the system costs; the method comprises the following steps that (a) a penalty cost and uncertain budget cost curve of a system under different robustness degrees is obtained; (b) a total operating cost curve under different robustness degrees;
FIG. 6 shows the wind power output of the system in an out-of-limit condition;
FIG. 7 shows the output of the unit under the optimal scheduling scheme;
FIG. 8 is a schematic structural diagram of a power grid adjustable robust optimization scheduling system considering wind power uncertainty distribution characteristics according to the present invention;
fig. 9 is a schematic structural diagram of an electronic device according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples.
It will be appreciated by those skilled in the art that the following examples are illustrative of the invention only and should not be taken as limiting the scope of the invention. The examples do not specify particular techniques or conditions, and are performed according to the techniques or conditions described in the literature in the art or according to the product specifications. The materials, equipment and the like used are all conventional products which can be obtained by purchasing and are not indicated by manufacturers.
The invention provides a power grid adjustable robust optimal scheduling method considering wind power uncertainty distribution characteristics, which comprises the following two steps:
step (1), firstly, a novel wind power output uncertain interval based on wind power fluctuation quantity distribution characteristics is constructed.
For the establishment of the uncertain interval, the robust optimization scheduling strategy provided by the invention comprises two modes, in short-term scheduling, the uncertain interval of the wind turbine generator output is constructed through a wind speed predicted value, and in ultra-short-term scheduling between two times of short-term scheduling, as the scheduling times are more, the data requirement is severer by using a wind speed predicted value method, and the construction cost of the uncertain interval is overlarge, the uncertain interval of the wind speed is constructed based on the probability distribution of the wind speed fluctuation quantity, and a specific flow chart is shown in figure 1; as long as the wind speed prediction error and the wind speed fluctuation can be absorbed within the confidence interval, the wind speed prediction error and the wind speed fluctuation can be absorbed as shown in the formulas (6) and (8).
The output power of the wind turbine generator is closely related to the wind speed, and the relationship between the output power and the wind speed is shown as a formula (31).
Figure BDA0002628371040000101
In the formula:
Figure BDA0002628371040000102
is the output power of fan j at time t,
Figure BDA0002628371040000103
the unit installed capacity, v, of fan jj,tThe wind speed at the time t of the position of the fan j,
Figure BDA0002628371040000104
and
Figure BDA0002628371040000105
respectively cut-in and cut-out wind speed, v, of fan jj,ratedThe rated wind speed of the fan j. The variation curve of the output power of the fan along with the wind speed is shown in figure 2.
In short-term scheduling, generally speaking, the predicted wind speed value may not be completely equal to the actual wind speed value, but a certain prediction error may exist. Therefore, the wind speed actual value is defined as the sum of the wind speed predicted value and the wind speed prediction error, as shown in the formula (32).
Figure BDA0002628371040000111
In the formula:
Figure BDA0002628371040000112
is a predicted value of wind speed and is a known quantity;
Figure BDA0002628371040000113
predicting error for wind speed, which is a random quantity;
Figure BDA0002628371040000114
the upper and lower limits of the wind speed prediction error are provided.
The wind speed prediction error can be regarded as a normally distributed random variable with a mean value of 0 and a standard deviation of σ, so the probability density function of the wind speed actual value obtained by equation (32) is:
Figure BDA0002628371040000115
in the formula:
Figure BDA0002628371040000116
the wind speed of the fan j at the time t is predicted, and the standard deviation of the wind speed prediction deviation is generally taken as tau percent of the predicted value, namely
Figure BDA0002628371040000117
In the ultra-short-term scheduling, if the wind speed sequence of the position of the fan j in the T time period is Vj，Vj＝[vj,1,vj,2,...,vj,t-1,vj,t,...,vj,T]Defining the wind speed at the time t +1 as the wind speed at the time t and the wind speed fluctuation delta vjAnd (3) the sum:
vj,t+1＝vj,t+Δvj,t (29)
the probability density function of the wind speed fluctuation amount can be obtained by means of probability fitting based on historical data. Assuming that the probability density function of the fluctuation amount of the wind speed is h (Δ v)j) Since the wind speed at the current time is a known quantity, the probability density function of the wind speed at time t +1 is expressed by equation (35).
Figure BDA0002628371040000118
In the formula (I), the compound is shown in the specification,
Figure BDA0002628371040000119
the probability density function of the wind speed at the t +1 moment at the j position of the wind turbine generator; v. ofj,tIs the wind speed v of the position of the fan j at the moment tj,t+1Is the wind speed v of the position of the fan j at the moment t +1jmaxIs the maximum wind speed.
The expression (35) shows that the probability distribution of the wind speed at the time t +1 obtained by the method in this section is a result of superposing a wind speed fluctuation amount probability density function with the wind speed at the time t as a base value, so that the correlation of the wind speeds at adjacent times can be kept.
And (2) constructing an optimization model capable of realizing coordinated optimization of robustness and economy based on the novel uncertain interval of wind power output provided in the step (1), and solving the model to obtain an optimal scheduling scheme of the power grid.
Because the robust optimization model is the optimal solution for solving the objective function under the worst condition, the probability of some worst conditions is considered to be very low, and the condition that the model can meet the constraint under any condition can be ensured, so that the solved power grid dispatching scheme is too conservative. In order to overcome the defect, the invention introduces a concept of robustness, and scales the uncertain interval through setting the robustness, so that the conservatism of an optimization model is reduced, and the aim of optimizing a scheduling scheme is fulfilled.
And scaling the uncertain interval of the wind power output by adopting the robustness, wherein the value range is [0,1 ]. Based on the value of the robustness, the upper and lower bounds of the scaled wind power output uncertain interval can be calculated according to the formulas (36) - (39). And in short-term scheduling, calculating the upper and lower bounds of the wind power output uncertain interval according to the expressions (36) - (37), and in ultra-short-term scheduling, calculating the upper and lower bounds of the wind power output uncertain interval according to the expressions (38) - (39). The smaller the robustness, the smaller the tolerable range of the optimized scheduling scheme to the uncertain variables. Taking the wind power uncertain interval based on the fluctuation amount of the wind speed as an example, when the wind speed uncertain interval is 0, the confidence interval of the wind speed is an empty set, namely the wind speed is considered to have no fluctuation, and the wind speed at the moment t +1 is the same as the wind speed at the moment t.
Figure BDA0002628371040000121
Figure BDA0002628371040000122
Figure BDA0002628371040000123
Figure BDA0002628371040000124
In the formula:
Figure BDA0002628371040000125
is the confidence interval of the wind speed at the t +1 moment under the robustness degree, vj,tIs the wind speed v of the position of the fan j at the moment tj,t+1The wind speed of the position of the fan j at the moment t +1 is obtained;
Figure BDA0002628371040000126
as a predicted value of wind speed, vjmaxIs the maximum wind speed.
Due to the uncertainty of wind power output, a power system needs to reserve a certain rotation reserve capacity for the system, and when the reserved rotation reserve capacity of the system is not enough to stabilize the fluctuation of the wind power output, the system generates the phenomenon of wind abandoning and load abandoning. However, the arrangement of the spinning reserve requires a certain cost, and the running economy is deteriorated due to the excessive reservation of the spinning reserve in the system. Therefore, the objective function of the optimization model is the sum of the total power generation cost of the system, the rotating standby setting cost of the system and the wind curtailment and load curtailment penalty cost of the system, and the objective structure is shown in formulas (40) to (41).
Figure BDA0002628371040000131
Figure BDA0002628371040000132
In the formula: c is the total operating cost of the system; cpThe total power generation cost of the system; cabandonThe total penalty cost of the system; creSetting cost for the system total rotation standby, namely the total uncertain budget cost; t is the total time period number of the scheduling; n is a conventional generator set;
Figure BDA0002628371040000133
active power is output for a conventional unit (such as a thermal power unit) i at the moment t,
Figure BDA0002628371040000134
for the kth energy storage device exchanging power with the power system at time t, Pt outFor exchanging power, P, between the electric power system and the external network at time twaste,t、Pcut,tRespectively, the wind abandoning power and the load abandoning power P of the system at the time tre,tThe system rotation reserve capacity is set for the scheduling scheme at the time t; f. ofpg(Pi,t) For the cost of electricity generation of conventional units, ai、bi、ciThe fuel cost coefficient of a conventional unit i; f. ofpo(Pt out) Is composed ofCost of exchanging power, κ, between the electric system and the external gridPOIs a unit exchange power cost coefficient;
Figure BDA0002628371040000135
for the charging and discharging costs of the energy storage device, kappaesIs a charge-discharge cost coefficient; f. ofwaste(Pwaste,t)、fcut(Pcut,t) Punishment cost, kappa, for wind abandon and load abandon respectivelywaste,t、κcut,tIs a penalty cost coefficient; f. ofre(Pre,t) Uncertainty of budget cost, κ, for power systemcut,tA cost factor for spinning reserve;
by solving the objective function, the model can obtain the optimal output combination of the conventional generator set, the energy storage device and the external power grid which minimize the total operation cost of the power system and the reserved up-regulation and down-regulation rotary reserve of each conventional generator set
Figure BDA0002628371040000136
Reserved up-regulation and down-regulation rotation standby P for external power gridt outu、Pt outd。
The constraints of the optimization model are as follows:
1) power balance constraint
Figure BDA0002628371040000141
In the formula: pL,tM, K are the sets of wind turbine generator and energy storage device respectively for the load power at time t.
2) Generator set output restraint
Figure BDA0002628371040000142
In the formula:
Figure BDA0002628371040000143
of conventional generator sets i respectivelyUpper and lower limits of output.
3) Ramp rate constraint of generator set
Figure BDA0002628371040000144
In the formula:
Figure BDA0002628371040000149
the maximum rising and falling rates of the conventional generator set i are respectively, and delta T is a scheduling time interval.
4) Rotational standby restraint for generator set
Figure BDA0002628371040000145
Figure BDA0002628371040000146
In the formula:
Figure BDA00026283710400001410
up-turn reserve and down-turn reserve, P, respectively, for a conventional generator set iimaxIs the upper limit of i output, P, of a conventional generator setiminI, setting the lower limit of output for the conventional generator set; .
5) External power grid and system exchange power constraint
Figure BDA0002628371040000147
In the formula:
Figure BDA00026283710400001411
and respectively exchanging the maximum and minimum values of power between the power system and an external power grid.
6) External power grid providing rotating standby constraint
Figure BDA0002628371040000148
In the formula: pt outu、Pt outdThe up-regulation rotary standby and the down-regulation rotary standby are respectively provided for an external power grid.
7) Output constraint accounting for spinning reserve
Figure BDA0002628371040000151
8) Energy storage device charge-discharge restraint
Figure BDA0002628371040000152
In the formula:
Figure BDA0002628371040000153
the maximum charge and discharge power of the energy storage device k, respectively.
9) Energy storage device state of charge constraint
Figure BDA0002628371040000154
In the formula: sk.tIs the state of charge of the energy storage device k at time t, Skmin、SkmaxRespectively, the upper limit and the lower limit of the state of charge of the energy storage device k.
In the constraint formula, the output of the wind turbine generator is an uncertain interval variable, so that the rotating reserve capacity and the output of each controllable unit of the system are set, and the conventional unit and the energy storage equipment can meet the system constraint when the wind turbine generator output has any value in the confidence interval described in the first step. And regarding the possible output of the wind turbine generator outside the confidence interval, the wind turbine generator is regarded as wind curtailment and load curtailment power as the system constraint may not be met.
According to the equation (31) and the equations (36) to (39), when the robustness is less than 1, the wind curtailment load shedding power of the power system is calculated as shown in the equations (52) to (54).
Figure BDA0002628371040000155
Figure BDA0002628371040000156
Figure BDA0002628371040000157
In the formula:
Figure BDA0002628371040000158
is a probability density function of the wind turbine j output at the time t obtained by the formulas (33) and (35), Pjcut,t、Pjwaste,tThe power of the wind abandon and the load abandon of the fan j at the time t,
Figure BDA0002628371040000159
and u (x) is a step function, and is the upper limit and the lower limit of the power output confidence interval of the fan j at the moment t.
According to the formula, the total penalty cost of the system is determined by the value of the robustness, and the total penalty cost of the system is a certain value under the condition that the robustness is not changed.
The output interval of the wind turbine generator at the t +1 moment is obtained by superposing the fluctuation quantity interval or the prediction error interval by using the output at the t moment or the prediction output at the t +1 moment, namely the known quantity, as a base value, and the robustness determines the interval size. Therefore, the optimization model provided by the invention requires that the scheduling output of the controllable unit of the system can be balanced with the known output, and the rotating reserve capacity of the system can meet the requirements of the fluctuation amount or the confidence interval of the prediction error.
Therefore, the power balance constraint is rewritten into the form of formula (55) and formula (56), and other constraints are not changed, the minimum total power generation cost min { C } of the system is solvedpThe optimal output set of each controllable device can be obtainedAnd min { C }pThe solution of (c) is transformed to a deterministic problem.
Figure BDA0002628371040000161
Figure BDA0002628371040000162
In the formula:
Figure BDA0002628371040000164
the conventional unit i outputs active power at the time t,
Figure BDA0002628371040000165
for the predicted output power of the wind turbine j at time t,
Figure BDA0002628371040000166
the actual output power of the wind turbine j at the moment t-1,
Figure BDA0002628371040000167
for the kth energy storage device exchanging power with the power system at time t, Pt outFor exchanging power, P, between the electric power system and the external network at time tL,tIs the load power at time t; n is the conventional generator set, and M, K is the set of wind turbine generator set and energy storage equipment respectively.
Since the system rotation reserve capacity needs to satisfy any possible wind power output within the confidence interval, the solution of the rotation reserve capacity is a robust optimization problem. The objective function of equation (40) can be rewritten as shown in equation (57) by the above analysis.
Figure BDA0002628371040000163
According to the constraint conditions, the maximum value and the minimum value which can be reached by the conventional units and the external network exchange power of the system according to the up-regulation and the down-regulation standbyAnd at least exceeding the upper and lower output limits of each controllable device due to the output change of the wind turbine generator. Therefore, for min { max { CreThe problem of solving the rotating reserve capacity of each controllable device is changed into the problem of solving the upper and lower bounds of the output of each controllable device, which can be reached due to the change of the output of the wind turbine generator.
Because the output of the wind power can meet the power balance constraint in the confidence interval if the output of the wind power can meet the power balance constraint in the limit condition in the confidence interval, the output upper and lower bounds of each unit should meet the formula (58).
Figure BDA0002628371040000171
In the formula:
Figure BDA0002628371040000174
Pt outrespectively representing the upper and lower limits of the output of each controllable device;
Figure BDA0002628371040000175
and the upper and lower boundaries of the wind turbine generator in the confidence interval.
By rewriting the constraint of equation (42) to equation (58), the uncertain variable can be converted into a deterministic variable and solved. The up-regulation and down-regulation rotation standby of the conventional generator set is calculated according to a formula (59) and a formula (60) of an up-regulation and down-regulation rotation standby calculation provided by an external power grid to a system.
Figure BDA0002628371040000172
Figure BDA0002628371040000173
In the formula:
Figure BDA0002628371040000176
is to solve min { C) according to the constraint formula (45) formula (46)pConventional unit output and system exchange power in the obtained system scheduling strategy
Obviously, with the increase of the robustness, the wind power output uncertainty interval is continuously increased, and the system uncertainty budget cost is gradually increased. As can be seen from the equation (52), the penalty cost of wind curtailment and load curtailment of the system is continuously reduced along with the increase of the robustness, and as can be seen from the equation (55) and the equation (56), the selection of the robustness during each scheduling has no influence on the total active power generation cost of the system, so that the model can obtain the optimal robustness during each scheduling, and the total operation cost of the system is minimized. And solving the robustness and the power grid dispatching strategy under the robustness through YALMIP, so that the economic efficiency and the robustness can be cooperatively optimized.
As shown in fig. 8, the system for jointly planning power transmission channels and energy storage considering economy and flexibility includes:
the first processing module 101 is used for constructing a novel wind power output uncertain interval based on wind power fluctuation quantity distribution characteristics; the establishment of the wind power output uncertain interval comprises two types: in short-term scheduling, an uncertain interval of wind turbine generator output is constructed through a wind speed predicted value, and the actual wind speed v at the next moment is constructed based on a wind speed predicted errorj,tA normal distribution probability density function; in ultra-short-term scheduling, an uncertain interval of wind turbine generator output is constructed based on current-time wind speed and wind speed fluctuation, and the actual wind speed v at the next moment is constructed according to the probability distribution of the wind speed fluctuation quantity obtained by statistical fittingj,tA probability density function of;
the second processing module 102 is configured to construct a power grid scheduling model for achieving coordinated optimization of robustness and economy based on the constructed uncertainty interval; the objective function of the power grid dispatching optimization model is that the sum of the total power generation cost of the system, the rotating standby setting cost of the system and the wind and load abandoning punishment cost is minimized;
and the scheduling module 103 is used for calculating the penalty cost and the uncertain budget cost based on the robustness, obtaining the optimal output combination of the conventional generator set, the energy storage device and the external power grid which minimizes the total operation cost of the system and the reserved up-regulation and down-regulation rotation reserve of each generator set by solving an objective function, and then scheduling according to the solving result.
According to the power transmission channel and energy storage combined planning system considering the economy and the flexibility, provided by the embodiment of the invention, the economy of system operation can be effectively improved, the wind power consumption capability of the system is enhanced, the influence of high-permeability renewable energy access on a power grid is solved, and the system is easy to popularize and apply.
The system provided by the embodiment of the present invention is used for executing the above method embodiments, and for details of the process and the details, reference is made to the above embodiments, which are not described herein again.
Fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and referring to fig. 9, the electronic device may include: a processor (processor)201, a communication Interface (communication Interface)202, a memory (memory)203 and a communication bus 204, wherein the processor 201, the communication Interface 202 and the memory 203 complete communication with each other through the communication bus 204. The processor 201 may call logic instructions in the memory 203 to perform the following method: constructing a novel wind power output uncertain interval based on wind power fluctuation quantity distribution characteristics; the establishment of the wind power output uncertain interval comprises two types: in short-term scheduling, an uncertain interval of wind turbine generator output is constructed through a wind speed predicted value, and the actual wind speed v at the next moment is constructed based on a wind speed predicted errorj,tA normal distribution probability density function; in ultra-short-term scheduling, an uncertain interval of wind turbine generator output is constructed based on current-time wind speed and wind speed fluctuation, and the actual wind speed v at the next moment is constructed according to the probability distribution of the wind speed fluctuation quantity obtained by statistical fittingj,tA probability density function of; based on the established uncertain interval, a power grid dispatching model for realizing coordinated optimization of robustness and economy is established; the objective function of the power grid dispatching optimization model is that the sum of the total power generation cost of the system, the rotating standby setting cost of the system and the wind and load abandoning punishment cost is minimized; then based on the calculation of the punishment cost and the uncertain budget cost of the robustness degree, the optimal output combination of the conventional generator set, the energy storage device and the external power grid, which minimizes the total operation cost of the system, is obtained by solving the objective functionAnd the units need to be reserved for up-regulation and down-regulation rotation for standby, and then are scheduled according to the solving result.
In addition, the logic instructions in the memory 203 may be implemented in the form of software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
On the other hand, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for performing the power grid adjustable robust optimized scheduling considering wind power uncertainty distribution characteristics provided in the foregoing embodiments, for example, the method includes constructing a novel wind power uncertainty output interval based on wind power fluctuation amount distribution characteristics; the establishment of the wind power output uncertain interval comprises two types: in short-term scheduling, an uncertain interval of wind turbine generator output is constructed through a wind speed predicted value, and the actual wind speed v at the next moment is constructed based on a wind speed predicted errorj,tA normal distribution probability density function; in ultra-short-term scheduling, an uncertain interval of wind turbine generator output is constructed based on current-time wind speed and wind speed fluctuation, and the actual wind speed v at the next moment is constructed according to the probability distribution of the wind speed fluctuation quantity obtained by statistical fittingj,tA probability density function of; based on the established uncertain interval, a power grid dispatching model for realizing coordinated optimization of robustness and economy is established; the objective function of the power grid dispatching optimization model is the total power generation cost of the system and the rotating standby setting cost of the systemAnd minimizing the sum of wind abandon and load abandon punishment costs; and then, based on the calculation of the punishment cost and the uncertain budget cost of the robustness, solving an objective function to obtain the optimal output combination of the conventional generator set, the energy storage device and the external power grid which minimizes the total operation cost of the system and the reserved up-regulation and down-regulation rotary reserve of each generator set, and then scheduling according to the solving result.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
In order to verify the effectiveness of the method, the wind power adjustable robust scheduling is carried out by taking a simplified power grid containing 3 conventional generator sets, 2 wind turbine sets and 1 energy storage device as an example.
The load of a power grid is set to 3000kW, the parameters of 3 conventional units are shown in Table 1, the capacity of the wind turbine unit 1 is 1200kW, the capacity of the wind turbine unit 2 is 800kW, the cut-in wind speed, the rated wind speed and the cut-out wind speed of the two units are all 3m/s, 20m/s and 25m/s, and the wind speed conditions are the same. The energy storage device is matched with the 800kW wind turbine generator set 2 for energy storage, the capacity of the energy storage device is 5% of the maximum output of the set per hour and is 40kWh, and the energy storage device and relevant parameters of power exchange between a power grid and an external power grid are shown in the table 2. The penalty cost of wind curtailment of the power grid is 61.32$/MWh, and the penalty cost of load curtailment is 104.2 $/MWh.
TABLE 1 conventional Generator set parameters
Figure BDA0002628371040000201
TABLE 2 external grid exchange power and energy storage related parameters
Figure BDA0002628371040000202
The wind speed data selects data of 60 days in total from 2016, 20 days in 3 months to 2016, 5 months and 19 days in 2016 in China, one day is randomly selected for verification of the invention, the total scheduling time is set to be 1d, the ultra-short-term scheduling step length is set to be 15min, the short-term scheduling step length is set to be 1h, the optimization model has a wind speed predicted value at the whole time from 0:00 to 23:00, the standard deviation of the wind speed predicted error is 9% of the predicted value, and the upper limit and the lower limit are 30% of the predicted value. The predicted wind speed and the current-day actual wind speed are shown in fig. 4, and the cauchy distribution is selected as the probability density function of the amount of fluctuation of the wind speed, as shown in equation (31).
Figure BDA0002628371040000211
In order to compare the influence of different robustness degrees on the total operation cost of the system, the model is solved under different robustness degrees. Because the economic magnitude of the single scheduling scheme is extremely small, the calculation example adopts a method of performing one-day scheduling under a certain robustness and accumulating the cost to enhance the significance of the result. The curve of the penalty cost of the system under different robustness degrees and the uncertain budget cost is shown in fig. 5(a), and as the robustness degree increases, the penalty cost of the system continuously decreases, but the uncertain budget cost gradually decreasesIt is gradually improved. The total operation cost of the system under different robustness degrees is shown in fig. 5(b), and under the condition that the conventional unit climbing rate constraint is less effective, the total power generation cost C of the system under different robustness degreespApproximately the same, the variation trend of the total operation cost is similar to the variation trend of the sum of the penalty cost and the uncertain budget cost, and the total operation cost is characterized by firstly decreasing and then increasing. The robustness of the system obtained by the invention with the minimum total operation cost is about 0.82.
The method comprises the steps of selecting 4 different robustness degrees which are respectively 0.00, 0.40 and 0.82 and most frequently used 0.95 (95% confidence interval), solving the proposed optimization model according to the four robustness degrees, and enabling the obtained scheduling scheme to bear the maximum wind power output and the minimum wind power output of the power grid and the actual wind power output of the power grid to be as shown in fig. 6. It can be seen that with the continuous increase of the robustness, the situation that the wind power output of the power grid is out of limit is continuously reduced.
The power grid is controlled according to the scheduling scheme obtained by the 4 robustness degrees, and the wind curtailment and load curtailment probability, the total penalty cost, the total undetermined budget cost and the total operation cost of the power grid in actual operation for one day are shown in table 3.
TABLE 3 System cost
Figure BDA0002628371040000212
Figure BDA0002628371040000221
The above table shows that the wind power fluctuation on the day is small due to calculation, and the penalty cost of the maximum wind curtailment and load curtailment is only 5.63% of the total operation cost, so that the cost difference of each scheme is small. But the optimal robustness obtained by selecting the method is 0.82, and the total operation cost can be reduced by 0.81%, 2.00% and 5.51% compared with the other three schemes.
And carrying out power grid control scheduling according to the optimal robustness of 0.82, wherein a conventional unit scheduling scheme for 6 hours in total from 9:00 to 15:00 is shown in fig. 7. The 3 bar graphs at each moment are respectively the dispatching output of the conventional units G1, G2 and G3 which are arranged in sequence, the black part is the lower output limit which can be reached by the lower dispatching standby calculated according to the dispatching, and the white part is the upper output limit which can be reached by the upper dispatching standby calculated according to the dispatching. Due to the presence of the energy storage means, conventional unit reservations up/down standby may not be needed at certain times.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (9)

1. A power grid adjustable robust optimization scheduling method considering wind power uncertainty distribution characteristics is characterized by comprising the following steps:
step (1), constructing a novel wind power output uncertain interval based on wind power fluctuation quantity distribution characteristics; the establishment of the wind power output uncertain interval comprises two types: in short-term scheduling, an uncertain interval of wind turbine generator output is constructed through a wind speed predicted value, and the actual wind speed v at the next moment is constructed based on a wind speed predicted errorj，tA normal distribution probability density function; in ultra-short-term scheduling, an uncertain interval of wind turbine generator output is constructed based on current-time wind speed and wind speed fluctuation, and the actual wind speed v at the next moment is constructed according to the probability distribution of the wind speed fluctuation quantity obtained by statistical fittingj，tA probability density function of;
step (2), constructing a power grid dispatching model for realizing coordinated optimization of robustness and economy based on the constructed uncertain interval; the objective function of the power grid dispatching optimization model is that the sum of the total power generation cost of the system, the rotating standby setting cost of the system and the wind and load abandoning punishment cost is minimized; and then, based on the calculation of the punishment cost and the uncertain budget cost of the robustness, solving an objective function to obtain the optimal output combination of the conventional generator set, the energy storage device and the external power grid which minimizes the total operation cost of the system and the reserved up-regulation and down-regulation rotary reserve of each generator set, and then scheduling according to the solving result.
2. The power grid adjustable robust optimization scheduling method considering wind power uncertainty distribution characteristics as claimed in claim 1, wherein the specific steps of step (1) include:
in short-term scheduling, the relationship between the output power of the wind turbine generator and the wind speed is shown as formula (1):
Figure FDA0002628371030000011
in the formula:
Figure FDA0002628371030000012
is the output power of fan j at time t,
Figure FDA0002628371030000013
the unit installed capacity, v, of fan jj，tThe wind speed at the time t of the position of the fan j,
Figure FDA0002628371030000014
and
Figure FDA0002628371030000015
respectively cut-in and cut-out wind speed, v, of fan jj，ratedThe rated wind speed of the fan j;
the wind speed actual value is the sum of the wind speed predicted value and the wind speed prediction error, and is shown in the formula (2);
Figure FDA0002628371030000016
in the formula:
Figure FDA0002628371030000017
is a predicted value of wind speed and is a known quantity;
Figure FDA0002628371030000018
predicting error for wind speed, which is a random quantity;
Figure FDA0002628371030000019
upper and lower limits of wind speed prediction error;
the wind speed prediction error is regarded as a normal distribution random variable with a mean value of 0 and a standard deviation of sigma, and the probability density function of the wind speed actual value obtained by the formula (2) is as follows:
Figure FDA0002628371030000021
in the formula:
Figure FDA0002628371030000022
the wind speed of the fan j at the time t is predicted, and the standard deviation of the wind speed prediction deviation is taken as tau percent of the predicted value, namely
In the ultra-short-term scheduling, if the wind speed sequence of the position of the fan j in the T time period is Vj，Vj＝[vj，1，vj，2，...，vj，t-1，vj，t，…，vj，T]Defining the wind speed at the time t +1 as the wind speed at the time t and the wind speed fluctuation delta vjAnd (3) the sum:
vj，t+1＝vj，t+Δvj，t (4)
assuming that the probability density function of the fluctuation amount of the wind speed is h (Δ v)j) Therefore, the probability density function of the wind speed at the time t +1 is shown by the formula (5);
Figure FDA0002628371030000024
in the formula (I), the compound is shown in the specification,
Figure FDA0002628371030000025
the probability density function of the wind speed at the t +1 moment at the j position of the wind turbine generator; v. ofj，tIs the wind speed v of the position of the fan j at the moment tj，t+1Is the wind speed v of the position of the fan j at the moment t +1jmaxIs the maximum wind speed.
3. The power grid adjustable robust optimization scheduling method considering wind power uncertainty distribution characteristics as claimed in claim 1, wherein the specific steps of step (2) include:
scaling the wind power output uncertain interval by using the robustness, wherein the value range is [0,1 ]; based on the value of the robustness, the upper and lower bounds of the scaled wind power output uncertain interval can be calculated according to the formulas (6) - (9); in short-term scheduling, calculating the upper and lower bounds of the wind power output uncertain interval according to the formulas (6) - (7), and in ultra-short-term scheduling, calculating the upper and lower bounds of the wind power output uncertain interval according to the formulas (8) - (9);
1) wind power uncertain interval based on wind speed fluctuation amount: when the wind speed is 0, the uncertain interval of the wind speed is an empty set, namely the wind speed is considered to have no fluctuation, and the wind speed at the moment t +1 is the same as the wind speed at the moment t;
Figure FDA0002628371030000031
Figure FDA0002628371030000032
Figure FDA0002628371030000033
Figure FDA0002628371030000034
in the formula:
Figure FDA0002628371030000035
the confidence interval of the wind speed at the t +1 moment under the robustness is obtained; v. ofj，tIs the wind speed v of the position of the fan j at the moment tj，t+1The wind speed of the position of the fan j at the moment t +1 is obtained;
Figure FDA0002628371030000036
as a predicted value of wind speed, vjmaxIs the maximum value of wind speed;
the objective function of the scheduling model is the minimum sum of the total power generation cost of the power system, the system rotation standby setting cost and the wind curtailment load curtailment cost, and the objective function is shown in formulas (10) to (11);
Figure FDA0002628371030000037
Figure FDA0002628371030000038
in the formula: c is the total operating cost of the system; cpThe total power generation cost of the system; cabandonThe total penalty cost of the system; creSetting cost for the system total rotation standby, namely the total uncertain budget cost; t is the total time period number of the scheduling; n is a conventional generator set;
Figure FDA0002628371030000039
active power is output for a conventional unit (such as a thermal power unit) i at the moment t,
Figure FDA0002628371030000041
for the kth energy storage device exchanging power with the power system at time t, Pt outIs time tExchange power, P, between the power system and the external gridwaste，t、Pcut，tRespectively, the wind abandoning power and the load abandoning power P of the system at the time tre，tThe system rotation reserve capacity is set for the scheduling scheme at the time t; f. ofpg(Pi，t) For the cost of electricity generation of conventional units, ai、bi、ciThe fuel cost coefficient of a conventional unit i; f. ofpo(Pt out) Exchanging power costs, κ, for an electric power system with an external gridPOIs a unit exchange power cost coefficient;
Figure FDA0002628371030000042
for the charging and discharging costs of the energy storage device, kappaesIs a charge-discharge cost coefficient; f. ofwaste(Pwaste，t)、fcut(Pcut，t) Punishment cost, kappa, for wind abandon and load abandon respectivelywaste，t、κcut，tIs a penalty cost coefficient; f. ofre(Pre，t) Uncertainty of budget cost, κ, for power systemcut，tA cost factor for spinning reserve;
solving the objective function to obtain the optimal output combination of the conventional generator set, the energy storage device and the external power grid which minimize the total operation cost of the power system and the reserved up-regulation and down-regulation rotation reserve of each conventional generator set
Figure FDA0002628371030000043
Reserved up-regulation and down-regulation rotation standby P for external power gridt outu、Pt outdAnd then scheduling according to the solving result.
4. The power grid adjustable robust optimization scheduling method considering wind power uncertainty distribution characteristics as claimed in claim 3, wherein the constraint conditions of the power grid scheduling model are as follows:
1) power balance constraint
Figure FDA0002628371030000044
In the formula: pL，tM, K are respectively a set of a wind turbine generator and an energy storage device for the load power at the moment t;
2) generator set output restraint
Figure FDA0002628371030000045
In the formula:
Figure FDA0002628371030000046
respectively representing the upper limit and the lower limit of the output of a conventional generator set i;
3) ramp rate constraint of generator set
Figure FDA0002628371030000047
In the formula:
Figure FDA0002628371030000048
respectively the maximum rising rate and the maximum falling rate of a conventional generator set i, and delta T is a scheduling time interval;
4) rotational standby restraint for generator set
Figure FDA0002628371030000049
Figure FDA0002628371030000051
In the formula:
Figure FDA0002628371030000052
up-turn reserve and down-turn reserve, P, respectively, for a conventional generator set iimaxFor conventional generator set i to outputUpper limit, PiminI, setting the lower limit of output for the conventional generator set;
5) external power grid and system exchange power constraint
Figure FDA0002628371030000053
In the formula:
Figure FDA0002628371030000054
respectively carrying out maximum and minimum values of power exchange between the power system and an external power grid;
6) external power grid providing rotating standby constraint
Figure FDA0002628371030000055
In the formula: pt outu、Pt outdThe up-regulation rotation standby and the down-regulation rotation standby are respectively provided for an external power grid;
7) output constraint accounting for spinning reserve
Figure FDA0002628371030000056
8) Energy storage device charge-discharge restraint
Figure FDA0002628371030000057
In the formula:
Figure FDA0002628371030000058
maximum charging and discharging power of the energy storage device k, respectively;
9) energy storage device state of charge constraint
Figure FDA0002628371030000059
In the formula: sk.tIs the state of charge of the energy storage device k at time t, Skmin、SkmaxRespectively, the upper limit and the lower limit of the state of charge of the energy storage device k.
5. The power grid adjustable robust optimization scheduling method considering wind power uncertainty distribution characteristics according to claim 3, characterized in that:
when the robustness is less than 1, calculating the curtailment wind-cutting load power of the power system as shown in the formulas (22) to (24);
Figure FDA00026283710300000510
Figure FDA0002628371030000061
Figure FDA0002628371030000062
in the formula:
Figure FDA0002628371030000063
is a probability density function of the wind turbine j output at the time t obtained by the formulas (3) and (5), Pjcut，t、Pjwaste，tThe power of the wind abandon and the load abandon of the fan j at the time t,
Figure FDA0002628371030000064
the upper limit and the lower limit of the power output confidence interval of the fan j at the moment t, and u (x) is a step function;
in the solution, the formula (12) is rewritten into the forms of the formula (25) and the formula (26);
Figure FDA0002628371030000065
Figure FDA0002628371030000066
in the formula:
Figure FDA0002628371030000067
the conventional unit i outputs active power at the time t,
Figure FDA0002628371030000068
for the predicted output power of the wind turbine j at time t,
Figure FDA0002628371030000069
the actual output power of the wind turbine j at the moment t-1,
Figure FDA00026283710300000610
for the kth energy storage device exchanging power with the power system at time t, Pt outFor exchanging power, P, between the electric power system and the external network at time tL，tIs the load power at time t; n is a conventional generator set, and M, K is a set of a wind turbine generator set and an energy storage device respectively;
the objective function is rewritten as shown in equation (27):
Figure FDA00026283710300000611
the upper and lower output bounds of each unit should satisfy the formula (28):
Figure FDA00026283710300000612
in the formula:
Figure FDA00026283710300000613
tP outrespectively representing the upper and lower limits of the output of each controllable device;
Figure FDA00026283710300000614
the upper and lower boundaries of the wind turbine generator in the confidence interval are defined;
the up-regulation and down-regulation rotation standby of a conventional generator set, and the up-regulation and down-regulation rotation standby calculation formulas provided by an external power grid to a system are shown as formulas (29) and (30);
Figure FDA0002628371030000071
Figure FDA0002628371030000072
in the formula:
Figure FDA0002628371030000073
is to solve for min { CpThe output of a conventional unit and the system exchange power in the obtained system scheduling strategy;
and solving the objective function to obtain the optimal output combination of the conventional generator set, the energy storage device and the external power grid which minimize the total operation cost of the system and the reserved up-regulation and down-regulation rotary standby of each generator set, and then scheduling according to the solving result.
6. The power grid adjustable robust optimization scheduling method considering wind power uncertainty distribution characteristics according to claim 5, characterized in that: solved by YALMIP.
7. The power grid adjustable robust optimization scheduling system considering wind power uncertainty distribution characteristics is characterized by comprising the following steps:
the first processing module is used for constructing a novel wind power output uncertain interval based on wind power fluctuation quantity distribution characteristics; the wind power output uncertain intervalThe establishment of (2) comprises two types: in short-term scheduling, an uncertain interval of wind turbine generator output is constructed through a wind speed predicted value, and the actual wind speed v at the next moment is constructed based on a wind speed predicted errorj，tA normal distribution probability density function; in ultra-short-term scheduling, an uncertain interval of wind turbine generator output is constructed based on current-time wind speed and wind speed fluctuation, and the actual wind speed v at the next moment is constructed according to the probability distribution of the wind speed fluctuation quantity obtained by statistical fittingj，tA probability density function of;
the second processing module is used for constructing a power grid dispatching model for realizing coordinated optimization of robustness and economy based on the constructed uncertain interval; the objective function of the power grid dispatching optimization model is that the sum of the total power generation cost of the system, the rotating standby setting cost of the system and the wind and load abandoning punishment cost is minimized;
and the scheduling module is used for calculating the punishment cost and the uncertain budget cost based on the robustness, obtaining the optimal output combination of the conventional generator set, the energy storage device and the external power grid which minimizes the total operation cost of the system and the reserved up-regulation and down-regulation rotary standby of each generator set by solving an objective function, and then scheduling according to the solving result.
8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the grid adjustable robust optimized scheduling method considering wind power uncertainty distribution characteristics as claimed in any of claims 1 to 6.
9. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the grid adjustable robust optimal scheduling method considering wind power uncertainty distribution characteristics according to any of claims 1 to 6.
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