CN112668184A - Method and device for calculating wind power outage risk value under typhoon disaster and storage medium - Google Patents

Method and device for calculating wind power outage risk value under typhoon disaster and storage medium Download PDF

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CN112668184A
CN112668184A CN202011600468.6A CN202011600468A CN112668184A CN 112668184 A CN112668184 A CN 112668184A CN 202011600468 A CN202011600468 A CN 202011600468A CN 112668184 A CN112668184 A CN 112668184A
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wind power
power plant
active
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calculating
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郁琛
常康
方日升
杨皖浙
刘韶峰
黄霆
黄道姗
张伟骏
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Fujian Electric Power Co Ltd
Nari Technology Co Ltd
State Grid Electric Power Research Institute
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Fujian Electric Power Co Ltd
Nari Technology Co Ltd
State Grid Electric Power Research Institute
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Abstract

The invention discloses a method, a device and a storage medium for calculating a wind power outage risk value under a typhoon disaster, belonging to the technical field of power systems, wherein the method comprises the following steps: calculating a difference value between the real-time active predicted value of the wind power plant and the shutdown active critical value of the wind power plant; referring to a pre-fitted wind power plant active prediction error probability distribution curve, and calculating the wind power plant outage probability according to the difference; and multiplying the outage probability of the wind power plant by the minimum load shedding cost of the power system containing the wind power plant, and calculating to obtain an outage risk value of the wind power plant. The method provides a theoretical basis for wind power outage risk assessment under typhoon disasters, can provide references for typhoon disaster defenses such as emergency repair material allocation, rapid emergency repair after failures and the like, and has certain theoretical value and engineering value.

Description

Method and device for calculating wind power outage risk value under typhoon disaster and storage medium
Technical Field
The invention relates to a method and a device for calculating a wind power outage risk value under a typhoon disaster and a storage medium, and belongs to the technical field of wind power plants.
Background
The output of the wind power plant has fluctuation due to the randomness and the fluctuation of the wind speed, and the stable operation of the system cannot be influenced by the small-amplitude fluctuation of the input power because the power system has certain stable redundancy. However, when a large number of wind turbines are synchronously switched out in strong wind, the output of the wind power plant may suddenly change from close to rated output to zero. When the wind permeability in the power grid is improved to a certain level, the severe fluctuation of large-scale wind power output can cause obvious impact on the safe and stable operation of the power grid.
At present, most of domestic and foreign researches concern how typhoon is modeled and extreme wind speed of typhoon is simulated, so that typhoon risk analysis is carried out on cities or regions, wind load basis is provided for structural designs of buildings, wind turbine generators and the like when typhoon comes, basic information is provided for disaster prevention and reduction of coastal cities, or damage of the typhoon to the wind turbine generators is qualitatively analyzed, and few documents quantitatively analyze influences of typhoon weather on wind power plant output power and power grid reliability.
Therefore, it is necessary to study a method for evaluating the risk of wind power outage in typhoon disasters.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, provides a method, a device and a storage medium for calculating a wind power outage risk value under a typhoon disaster, and provides a theoretical basis for wind power outage risk assessment under the typhoon disaster.
In order to achieve the purpose, the invention is realized by adopting the following technical scheme:
in a first aspect, the present invention provides a method for calculating a wind farm outage risk value in a typhoon disaster, the method comprising the steps of:
calculating a difference value between the real-time active predicted value of the wind power plant and the shutdown active critical value of the wind power plant;
referring to a pre-fitted wind power plant active prediction error probability distribution curve, and calculating the wind power plant outage probability according to the difference;
and multiplying the outage probability of the wind power plant by the minimum load shedding cost of the power system containing the wind power plant, and calculating to obtain an outage risk value of the wind power plant.
With reference to the first aspect, further, the fitting method for the wind farm active prediction error probability distribution curve includes the following steps:
acquiring historical data of a wind power plant during typhoon;
calculating an active single-step prediction error of the wind power plant by using a continuous prediction method according to an active prediction value of the wind power plant before the wind power plant stops in historical data of the wind power plant during the typhoon and an active actual value of the wind power plant at a corresponding moment;
and fitting the probability distribution curve of the active prediction error of the wind power plant by adopting a t location-scale function according to the active single-step prediction error of the wind power plant.
With reference to the first aspect, further, the wind farm outage probability is calculated and obtained by using the following formula:
Figure BDA0002869160760000021
in the formula, PfRepresenting wind farm outage probability, xtRepresenting the real-time active predicted value, x, of the wind farmcThe wind power plant outage active critical value is represented, and f (x) represents an error probability distribution function.
With reference to the first aspect, further, the method for calculating the minimum load shedding cost includes the following steps:
taking the active power of the load shedding amount of each load node as a particle, taking the load shedding cost as a fitness function, and calculating by adopting a self-adaptive particle swarm optimization algorithm to obtain the load shedding amount of each load node corresponding to the minimum load shedding cost;
and multiplying the load shedding amount of each load node by the unit cost of the load shedding of the corresponding load node, and calculating to obtain the minimum load shedding cost.
With reference to the first aspect, further, the operating constraints in the optimal power flow calculation include:
and (3) load node voltage amplitude constraint:
Vimin≤Vi≤Vimax
in the formula, ViRepresenting the voltage amplitude of the load node i; vimaxRepresents the upper voltage amplitude limit, V, of the load node iiminRepresenting the lower voltage amplitude limit of the load node i;
load node load shedding amount constraint:
0≤PCi≤PDi
in the formula, PCiRepresenting the load shedding amount at the load node i; pDiRepresenting the load amount on the load node i;
branch tidal current safety constraint:
|Pij|≤Pijmax
in the formula, PijRepresenting the active power, P, of branch i-jijmaxRepresents the branch i-j power ceiling;
load node power balance equation:
Figure BDA0002869160760000031
Figure BDA0002869160760000032
Pij＝-Vi 2Gij+ViVj(Gijcosθij+βijsinθij)
in the formula, thetaij＝θi-θjThe phase angle difference between the load node i and the load node j is obtained; vi、θiThe voltage amplitude and the phase angle of the load node i are respectively; pGi、QGiRespectively representing the active output and the reactive output of the generator at a load node i; pDi、QDiRespectively representing the active load quantity and the reactive load quantity of the load node i; gijAnd betaijRespectively representing corresponding elements in the power system admittance matrix; and N is the total number of the load nodes.
And when the particle swarm algorithm is adopted for iterative solution, the updated particles are checked by adopting the constraint, and when the particles do not meet the constraint, the iteration is continued until a load shedding scheme corresponding to the particles meeting the constraint is obtained.
In a second aspect, the present invention provides an apparatus for calculating a risk value of a wind power outage in a typhoon disaster, the apparatus comprising:
a first calculation module: the wind power plant real-time active power prediction value and the wind power plant outage active power critical value are calculated;
a second calculation module: the wind power plant active prediction error probability distribution curve is used for referring to a pre-fitted wind power plant active prediction error probability distribution curve, and the wind power plant outage probability is calculated according to the difference value;
a third calculation module: and the wind power plant outage risk value is calculated by multiplying the wind power plant outage probability by the minimum load shedding cost of the power system including the wind power plant.
With reference to the second aspect, further, the wind power outage risk value calculation device in a typhoon disaster further includes:
an acquisition module: the system is used for acquiring historical data of the wind power plant during typhoon;
a fourth calculation module: the method is used for calculating the active single-step prediction error of the wind power plant by applying a continuous prediction method according to the active prediction value of the wind power plant before the wind power plant stops in the historical data of the wind power plant during the typhoon and the active actual value of the wind power plant at the corresponding moment;
a fitting module: and fitting the probability distribution curve of the active prediction error of the wind power plant by adopting a t location-scale function according to the active single-step prediction error of the wind power plant.
With reference to the second aspect, further, the wind power outage risk value calculation device in a typhoon disaster further includes:
load shedding amount calculation module: taking the active power of the load shedding amount of each load node as a particle, taking the load shedding cost as a fitness function, and calculating by adopting a self-adaptive particle swarm optimization algorithm to obtain the load shedding amount of each load node corresponding to the minimum load shedding cost;
a minimum load shedding cost calculation module: and multiplying the load shedding amount of each load node by the unit cost of the load shedding of the corresponding load node, and calculating to obtain the minimum load shedding cost.
In a third aspect, the invention further provides a device for calculating a risk value of outage of wind power under a typhoon disaster, which comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of the preceding claims.
In a fourth aspect, the invention also provides a computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the steps of the method of any of the preceding claims.
Compared with the prior art, the invention has the following beneficial effects:
calculating a difference value between a real-time active predicted value and a shutdown active critical value of a wind power plant, referring to a pre-fitted wind power plant active prediction error probability distribution curve, and calculating a shutdown probability of the wind power plant according to the difference value; finally, the outage probability of the wind power plant is multiplied by the minimum load shedding cost, the outage risk value of the wind power plant is obtained through calculation, a theoretical basis is provided for wind power plant outage risk assessment under typhoon disasters, references can be provided for typhoon disaster defenses such as emergency repair material allocation and rapid emergency repair after faults, and the method has certain theoretical value and engineering value.
Drawings
Fig. 1 is a flowchart of a method for calculating a wind outage risk value in a typhoon disaster according to an embodiment of the present invention;
FIG. 2 is a diagram of a modified IEEE33 load node testing system according to an embodiment of the present invention;
FIG. 3 is a wind power prediction error fitting graph provided by an embodiment of the present invention;
fig. 4 is a wind power prediction diagram during a typhoon period according to an embodiment of the present invention;
fig. 5 is a graph illustrating voltage amplitude changes of load nodes according to an embodiment of the present invention;
fig. 6 is a diagram illustrating a system minimum load shedding amount according to an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The first embodiment is as follows:
as shown in fig. 1, a method for calculating a wind outage risk value under a typhoon disaster includes:
the method comprises the following steps: calculating a difference value between the real-time active predicted value of the wind power plant and the shutdown active critical value of the wind power plant;
step two: referring to a pre-fitted wind power plant active prediction error probability distribution curve, and calculating the wind power plant outage probability according to the difference;
step three: and multiplying the outage probability of the wind power plant by the minimum load shedding cost of the power system containing the wind power plant, and calculating to obtain an outage risk value of the wind power plant.
In the embodiment of the invention, the historical maximum output of the wind power plant can be selected as the shutdown active critical value of the wind power plant, and the real-time active predicted value of the wind power plant can be obtained by the current power grid monitoring system.
In the second step, the fitting method of the probability distribution curve of the active prediction error of the wind power plant comprises the following steps:
step 201: acquiring historical data of a wind power plant during typhoon;
step 202: acquiring an active predicted value of the wind power plant before the wind power plant stops and an active actual value of the wind power plant at a corresponding moment from historical data of the wind power plant during typhoon;
step 203: calculating the active single-step prediction error of the wind power plant by using a continuous prediction method:
the calculation formula of the active single-step prediction error of the wind power plant is as follows:
et＝(PMt-PPt)/a
in the formula, PMtRepresenting the actual output power, P, of the wind farm at time tPtAnd (4) representing the predicted power of the wind power plant at the time t, wherein a is the number of wind power plant fans.
Step 204: and fitting the probability distribution curve of the active prediction error of the wind power plant by adopting a t location-scale function according to the active single-step prediction error of the wind power plant.
The method comprises the following steps of obtaining the probability of the active prediction error of the wind power plant at the current moment by adopting a probability distribution curve of the active prediction error of the wind power plant, and obtaining the outage probability of the wind power plant at the current moment by calculating the cumulative probability of the active prediction error of the wind power plant at the current moment to infinity, wherein a specific calculation formula is as follows:
Figure BDA0002869160760000071
in the formula, PfRepresenting wind farm outage probability, xtRepresenting the active predicted value x of the wind power plant at the moment tcThe wind power plant outage active critical value is represented, and f (x) represents an error probability distribution function.
The minimum load shedding cost is calculated and obtained by adopting a self-adaptive particle swarm optimization algorithm, and the method specifically comprises the following steps:
step 301: performing optimal power flow calculation by taking the minimum load shedding cost of the power system including the wind power plant as a target function to obtain initial parameters of a self-adaptive particle swarm optimization algorithm; wherein the expression of the objective function is as follows:
Figure BDA0002869160760000072
wherein n represents the number of all load nodes, ciRepresents the load shedding cost, P, of the i-load nodeCiCutting off the active power of the load for the i load node;
the operating constraints in the optimal power flow calculation are as follows:
and (3) load node voltage amplitude constraint:
Vimin≤Vi≤Vimax
in the formula, ViRepresenting the voltage amplitude of the load node i; vimaxRepresents the upper voltage amplitude limit, V, of the load node iiminRepresenting the lower voltage amplitude limit of the load node i;
load node load shedding amount constraint:
0≤PCi≤PDi
in the formula, PCiRepresenting the load shedding amount at the load node i; pDiRepresenting the load amount on the load node i;
branch tidal current safety constraint:
|Pij|≤Pijmax
in the formula, PijRepresenting the active power, P, of branch i-jijmaxRepresents the branch i-j power ceiling;
load node power balance equation:
Figure BDA0002869160760000081
Figure BDA0002869160760000082
Pij＝-Vi 2Gij+ViVj(Gij cos θij+βij sin θij)
in the formula, thetaij＝θi-θjThe phase angle difference between the load node i and the load node j is obtained; vi、θiThe voltage amplitude and the phase angle of the load node i are respectively; pGi、QGiRespectively representing the active output and the reactive output of the generator at a load node i; pDi、QDiRespectively representing the active load quantity and the reactive load quantity of the load node i; gijAnd betaijRespectively representing corresponding elements in the power system admittance matrix; and N is the total number of the load nodes.
And when the particle swarm algorithm is adopted for iterative solution, the updated particles are checked by adopting the constraint, and when the particles do not meet the constraint, the iteration is continued until a load shedding scheme corresponding to the particles meeting the constraint is obtained.
Step 302: taking the active power of the load shedding amount of each load node as a particle, taking the load shedding cost as a fitness function, performing particle swarm optimization, and obtaining the load shedding amount of each load node corresponding to the minimum load shedding cost; the specific method comprises the following steps:
the initial population is set to a plurality of N-1-dimensional vectors, and each particle represents a load shedding scheme, namely the load shedding amount of each load node. The adaptability value of each particle is expressed by the total cost of load shedding of the power distribution network:
Figure BDA0002869160760000083
in the formula, PCiThe active load amount is cut off for the load node i; c. CiLoad node i is the unit cost of load shedding; and e is the load shedding total cost of the power distribution network.
By tracking the individual historical optimum position P in each iterationg kAnd group historical best position Pq kUpdating the speed and the position of the particle to obtain the optimal fitness e of the population, namely the fitness e of the particle corresponding to the minimum load shedding cost;
the update formulas of speed and position are respectively:
Figure BDA0002869160760000091
Figure BDA0002869160760000092
in the formula, Vm kThe moving speed in the kth iteration of the mth particle; pq kFor the historical best position of the individual in the kth iteration, Pg kFor the historical best position in the kth iteration of the population, h1And h2Taking a non-negative constant as an acceleration factor; h is1And h2Is a random number between 0 and 1, Xm kIs the position in the kth iteration of the mth particle; w is a non-negative number, called a power constant, that controls the effect of the previous speed on the current speed. when w is larger, the influence of the previous speed is larger, and the global search capability is stronger; when w is smaller, the former speed is smaller, and the local search capability is stronger. In order to balance the global searching capability and the local improvement capability of the particle swarm optimization, the nonlinear dynamic inertia weight is adopted for improvementThe weight coefficient formula is expressed as:
Figure BDA0002869160760000093
in the formula, wmaxAnd wminMaximum and minimum inertial weight, respectively, e representing the current fitness value of the particle, eavgAnd eminRespectively representing the average fitness value and the minimum fitness value of all the current particles. In the algorithm, w is called adaptive weight because the inertial weight automatically changes with the objective function value of the particle.
Recalculating the fitness value e of the new particle, updating Pq kAnd Pg kK is k +1, when the number of iterations reaches kmaxAnd stopping iteration, otherwise, updating the speed and the position of the particles again for calculation to obtain the particles corresponding to the minimum fitness value, and taking the particles as a load shedding scheme after the wind power plant stops running.
Step 303: and multiplying the load shedding amount by the selected load shedding unit price, and calculating to obtain the minimum load shedding cost.
In the embodiment of the invention, the wind power plant outage probability is used as the risk probability, the minimum load shedding cost is used as the risk loss, and the wind power plant outage risk value can be obtained by multiplying the risk probability and the minimum load shedding cost, wherein the calculation formula is as follows:
R＝Pf·Cf
wherein R represents a wind farm outage risk value, PfRepresenting wind farm outage probability, CfRepresenting the minimum load shedding cost of the power system containing said wind farm.
It should be noted that, in the prior art, a comprehensive value of the risk occurrence probability and the loss after the risk occurrence is generally used as a risk exposure value (i.e., a risk value), and in the embodiment of the present invention, the calculation method of the wind farm outage risk value is not a calculation rule set by the applicant.
According to the method, the difference value between the real-time active prediction value of the wind power plant and the outage active critical value of the wind power plant is calculated, a pre-fitted wind power plant active prediction error probability distribution curve is referred, and the outage probability of the wind power plant is calculated according to the difference value; finally, the outage probability of the wind power plant is multiplied by the minimum load shedding cost, the outage risk value of the wind power plant is obtained through calculation, a theoretical basis is provided for wind power plant outage risk assessment under typhoon disasters, references can be provided for typhoon disaster defenses such as emergency repair material allocation and rapid emergency repair after faults, and the method has certain theoretical value and engineering value.
Example two:
in the embodiment, an example analysis is performed through an adjusted IEEE33 load node system including a wind farm, and the system structure is as shown in fig. 2, and the system accesses the wind farm with a rated power of 62MW at a load node 8. The load node 0 is a balanced load node, the voltage amplitude is set to be 1.05(p.u.), and the voltage safety range is 0.93-1.07 (p.u.).
The method for obtaining the wind power prediction error distribution by utilizing the historical data of a certain offshore wind field in Fujian province during typhoon is shown in the attached drawing 3, wherein the abscissa in the drawing represents the output power of the wind power plant, and the ordinate represents the error probability density. FIG. 4 shows the predicted power of a wind farm at a certain day during a typhoon, with the predicted time on the abscissa and the active predicted value of the wind farm on the ordinate, at 15 minute intervals. According to statistics, the historical maximum power of the wind power plant is 61MW, namely the critical power. According to the attached fig. 3, the outage probability of the wind farm at each moment can be calculated, taking 10:00 as an example, the calculation formula is as follows:
Figure BDA0002869160760000111
in the formula, PfRepresenting wind farm outage probability, xtRepresenting the active predicted value of the wind power plant at the time t, x at 10:00tTo 44.3113, f (x) represents the error probability distribution function, calculated to have a outage probability of 0.0823.
Wind power outage probabilities of 15:00 and 20:00 are calculated by referring to the calculation formula, and are specifically shown in table 1.
TABLE 1 wind farm outage probability
Time Probability of outage Wind speed (m/s)
10:00 0.0823 10.3
15:00 0.0902 10.4
20:00 0.0696 9.2
And after the wind power plant stops running, the system cuts load. The voltage amplitude of each load node before and after load shedding at 10:00 hours changes as shown in fig. 5, wherein the abscissa represents the node and the ordinate represents the voltage amplitude of the node. The load shedding amounts of the coincidence nodes 7, 8, 11, 13, 15 and 16 calculated by the adaptive particle swarm optimization algorithm are shown in table 2.
Load shedding amount of each load node in table 210: 00
Figure BDA0002869160760000112
Figure BDA0002869160760000121
Assuming that the load shedding unit costs of the load nodes 1-9, 10-21, 22-33 are 2,3,4 (ten thousand yuan/MW) respectively,
taking 10:00 as an example, the load shedding cost is calculated by adopting the following calculation formula:
Figure BDA0002869160760000122
in the formula, PCiThe load shedding amount of the load node i is; c. CiLoad node i is the unit cost of load shedding; n is the total number of the load nodes; e is the total load shedding cost of the power distribution network, and 10 is obtained by calculation: the load cut cost of the system at 00 hours is 593.52 ten thousand yuan/MW.
By using the above formula, the load shedding costs of 15:00 and 20:00 are calculated, and then the load shedding costs of the system at each time can be calculated as shown in table 3.
TABLE 3 off load cost
Time Tangential load capacity (MW) Tangential load cost (Wanyuan/MW)
10:00 177.26 593.52
15:00 144.91 465.73
20:00 160.94 518.76
The wind power outage risk value calculated by multiplying the wind power plant outage probability shown in table 1 by the corresponding time shedding load cost is shown in table 4.
TABLE 4 wind-powered outage Risk values
Time Value of risk
10:00 48.85
15:00 42.01
20:00 36.11
According to the wind power outage risk value under the typhoon condition, references can be provided for pre-disaster power grid risk defense and post-disaster scheduling and first-aid repair.
Example three:
the embodiment of the invention provides a device for calculating a risk value of wind power outage in typhoon disasters, which can be used for executing the method steps in the first embodiment, and the device comprises:
a first calculation module: the wind power plant real-time active power prediction value and the wind power plant outage active power critical value are calculated;
a second calculation module: the wind power plant active prediction error probability distribution curve is used for referring to a pre-fitted wind power plant active prediction error probability distribution curve, and the wind power plant outage probability is calculated according to the difference value;
a third calculation module: and the wind power plant outage risk value is calculated by multiplying the wind power plant outage probability by the minimum load shedding cost of the power system including the wind power plant.
The wind power outage risk value calculation device under the typhoon disaster further comprises:
an acquisition module: the system is used for acquiring historical data of the wind power plant during typhoon;
a fourth calculation module: the method is used for calculating the active single-step prediction error of the wind power plant by applying a continuous prediction method according to the active prediction value of the wind power plant before the wind power plant stops in the historical data of the wind power plant during the typhoon and the active actual value of the wind power plant at the corresponding moment;
a fitting module: and fitting the probability distribution curve of the active prediction error of the wind power plant by adopting a t location-scale function according to the active single-step prediction error of the wind power plant.
The wind power outage risk value calculation device under the typhoon disaster further comprises:
load shedding amount calculation module: taking the active power of the load shedding amount of each load node as a particle, taking the load shedding cost as a fitness function, and calculating by adopting a self-adaptive particle swarm optimization algorithm to obtain the load shedding amount of each load node corresponding to the minimum load shedding cost;
a minimum load shedding cost calculation module: and multiplying the load shedding amount of each load node by the unit cost of the load shedding of the corresponding load node, and calculating to obtain the minimum load shedding cost.
Example four:
the embodiment of the invention also provides a device for calculating the risk value of the wind power outage in the typhoon disaster, which comprises a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method of embodiment one.
Example five:
embodiments of the present invention also provide a computer-readable storage medium, on which a computer program is stored, which, when executed by a processor, implements the steps of the method of an embodiment.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (10)

1. A method for calculating a wind power plant outage risk value under a typhoon disaster is characterized by comprising the following steps:
calculating a difference value between the real-time active predicted value of the wind power plant and the shutdown active critical value of the wind power plant;
referring to a pre-fitted wind power plant active prediction error probability distribution curve, and calculating the wind power plant outage probability according to the difference;
and multiplying the outage probability of the wind power plant by the minimum load shedding cost of the power system containing the wind power plant, and calculating to obtain an outage risk value of the wind power plant.
2. The method for calculating the wind power outage risk value under the typhoon disaster according to the claim 1, wherein the method for fitting the wind power plant active prediction error probability distribution curve comprises the following steps:
acquiring historical data of a wind power plant during typhoon;
calculating an active single-step prediction error of the wind power plant by using a continuous prediction method according to an active prediction value of the wind power plant before the wind power plant stops in historical data of the wind power plant during the typhoon and an active actual value of the wind power plant at a corresponding moment;
and fitting the probability distribution curve of the active prediction error of the wind power plant by adopting a tlocation-scale function according to the active single-step prediction error of the wind power plant.
3. The method for calculating the wind power outage risk value under the typhoon disaster according to claim 2, wherein the wind power plant outage probability is calculated and obtained by adopting the following formula:
Figure FDA0002869160750000011
in the formula, PfRepresenting wind farm outage probability, xtRepresenting the active predicted value x of the wind power plant at the moment tcThe wind power plant outage active critical value is represented, and f (x) represents an error probability distribution function.
4. The method for calculating the wind outage risk value under the typhoon disaster according to claim 1, wherein the method for calculating the minimum load shedding cost comprises the steps of:
taking the active power of the load shedding amount of each load node as a particle, taking the load shedding cost as a fitness function, and calculating by adopting a self-adaptive particle swarm optimization algorithm to obtain the load shedding amount of each load node corresponding to the minimum load shedding cost;
and multiplying the load shedding amount of each load node by the unit cost of the load shedding of the corresponding load node, and calculating to obtain the minimum load shedding cost.
5. The method for calculating the wind power outage risk value under the typhoon disaster according to the claim 4, wherein the operation constraint conditions in the optimal power flow calculation comprise:
and (3) load node voltage amplitude constraint:
Vimin≤Vi≤Vimax
in the formula, ViRepresenting the voltage amplitude of the load node i; vimaxRepresents the upper voltage amplitude limit, V, of the load node iiminRepresenting the lower voltage amplitude limit of the load node i;
load node load shedding amount constraint:
0≤PCi≤PDi
in the formula, PCiRepresenting the load shedding amount at the load node i; pDiRepresenting the load amount on the load node i;
branch tidal current safety constraint:
|Pij|≤Pijmax
in the formula, PijTo representActive power of branch i-j, PijmaxRepresents the branch i-j power ceiling;
load node power balance equation:
Figure FDA0002869160750000021
Figure FDA0002869160750000022
Pij＝-Vi 2Gij+ViVj(Gijcosθij+βijsinθij)
in the formula, thetaij＝θi-θjThe phase angle difference between the load node i and the load node j is obtained; vi、θiThe voltage amplitude and the phase angle of the load node i are respectively; pGi、QGiRespectively representing the active output and the reactive output of the generator at a load node i; pDi、QDiRespectively representing the active load quantity and the reactive load quantity of the load node i; gijAnd betaijRespectively representing corresponding elements in the power system admittance matrix; n is the total number of the load nodes;
and when the particle swarm algorithm is adopted for iterative solution, the updated particles are checked by adopting the constraint, and when the particles do not meet the constraint, the iteration is continued until a load shedding scheme corresponding to the particles meeting the constraint is obtained.
6. The utility model provides a wind-powered electricity generation outage risk value calculating device under typhoon calamity which characterized in that, the device includes:
a first calculation module: the wind power plant real-time active power prediction value and the wind power plant outage active power critical value are calculated;
a second calculation module: the wind power plant active prediction error probability distribution curve is used for referring to a pre-fitted wind power plant active prediction error probability distribution curve, and the wind power plant outage probability is calculated according to the difference value;
a third calculation module: and the wind power plant outage risk value is calculated by multiplying the wind power plant outage probability by the minimum load shedding cost of the power system containing the wind power plant.
7. The apparatus for calculating a risk value of wind power outage under a typhoon disaster according to claim 5, further comprising:
an acquisition module: the system is used for acquiring historical data of the wind power plant during typhoon;
a fourth calculation module: the method is used for calculating the active single-step prediction error of the wind power plant by applying a continuous prediction method according to the active prediction value of the wind power plant before the wind power plant stops in the historical data of the wind power plant during the typhoon and the active actual value of the wind power plant at the corresponding moment;
a fitting module: and fitting the probability distribution curve of the wind power plant active prediction error by adopting a tlocation-scale function according to the wind power plant active single-step prediction error.
8. The apparatus for calculating a risk value of wind power outage under a typhoon disaster according to claim 5, further comprising:
load shedding amount calculation module: taking the active power of the load shedding amount of each load node as a particle, taking the load shedding cost as a fitness function, and calculating by adopting a self-adaptive particle swarm optimization algorithm to obtain the load shedding amount of each load node corresponding to the minimum load shedding cost;
a minimum load shedding cost calculation module: and multiplying the load shedding amount of each load node by the unit cost of the load shedding of the corresponding load node, and calculating to obtain the minimum load shedding cost.
9. A wind power outage risk value calculating device under a typhoon disaster is characterized by comprising a processor and a storage medium;
the storage medium is used for storing instructions;
the processor is configured to operate in accordance with the instructions to perform the steps of the method according to any one of claims 1 to 5.
10. Computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the steps of the method of any one of claims 1 to 5.
CN202011600468.6A 2020-12-29 2020-12-29 Method and device for calculating wind power outage risk value under typhoon disaster and storage medium Pending CN112668184A (en)

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