CN117498353B - Voltage support adjustment method and system for new energy station grid-connected system - Google Patents
Voltage support adjustment method and system for new energy station grid-connected system Download PDFInfo
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Abstract
The invention discloses a voltage support adjustment method and a system for a new energy station grid-connected system, which are characterized in that firstly, the influence of the output change of a new energy station on the voltage of a key node is quantified by utilizing a sensitivity relation, then, a regulation and control mathematical model aiming at minimizing the voltage change of the key node is constructed on the basis of the influence, and the initial position of a population can adapt to different new energy stations by introducing a search space distribution strategy and an optimized marine predator algorithm, so that the global search capacity and the search efficiency can be improved, and the output of the new energy station corresponding to the optimal solution is calculated, thereby obtaining a voltage support adjustment strategy.
Description
Technical Field
The invention relates to the field of data processing, in particular to a voltage support adjustment method and system for a grid-connected system of a new energy station.
Background
The new energy station is developed on a large scale, and meanwhile, a great challenge is brought to the stable operation of the power grid, and the voltage stable operation of the power system is adversely affected by inherent factors such as randomness and uncertainty. Therefore, the research on the voltage supporting capability of the new energy station grid-connected system and the improvement on the voltage safety stability of the new energy station connected to the power grid are important realistic demands for the operation of the power system.
When a large number of new energy stations are connected into the system, the voltage supporting capability is weak, the problem of voltage instability is possibly caused, and the academic world and the engineering world are subjected to more researches on the supporting strength of the grid-connected system, and the method comprises a voltage supporting scheme and an evaluation scheme of the voltage supporting capability, such as a new energy grid-connected system voltage supporting strength evaluation method and system disclosed by publication No. CN113904375A, a new energy transient voltage supporting capability evaluation method disclosed by publication No. CN115936478A based on a short circuit ratio and an impedance ratio, and the like.
However, in the prior art, when the voltage supporting capability is evaluated based on various parameters, since the output of the new energy station is affected by various factors, the output calculation involves a plurality of parameters and complex relationships, so that the evaluation result cannot be directly applied to controlling the output of the actual new energy station, and therefore the voltage supporting capability of the grid-connected system of the new energy station cannot be directly improved.
Disclosure of Invention
Aiming at the problem that the voltage supporting capability of the grid-connected system of the new energy station is difficult to improve by controlling the output in the prior art, the invention provides the voltage supporting adjustment method and system of the grid-connected system of the new energy station, and the parameter and complex relation related to the output calculation are described by establishing a regulating mathematical model, so that the initial position of the population can adapt to different new energy stations, the marine predator algorithm is adjusted by the searching spatial distribution strategy, the global searching capability and searching efficiency can be improved, and the output of the new energy station corresponding to the optimal solution is calculated, thereby obtaining the voltage supporting adjustment strategy.
The following is a technical scheme of the invention.
The voltage support adjustment method for the grid-connected system of the new energy station comprises the following steps:
s1: acquiring parameter information of a new energy station grid-connected system, and determining a first-order and second-order sensitivity relationship between key node power and new energy station output;
s2: determining a relation function between the voltage change of the key node and the output of the new energy station according to the sensitivity relation and the line parameter of the node;
s3: setting constraint conditions, and establishing a regulating mathematical model of the voltage support of the key node by taking the minimum voltage change of the key node as a target based on the relation function;
s4: determining a search space distribution strategy according to the historical output;
s5: adjusting a marine predator algorithm based on a search space distribution strategy, and performing optimization calculation on a control mathematical model to obtain an optimal solution;
s6: and outputting a voltage support adjustment strategy according to the output of the new energy station corresponding to the optimal solution.
According to the invention, through acquiring the parameter information of the system, the running state and the characteristics of the system can be better understood, the sensitivity relation between the power of the key node and the output of the new energy station is determined, the influence of the output change of the new energy station on the voltage of the key node can be known, and a decision basis is provided for a subsequent voltage support adjustment strategy. By establishing a relation function between the voltage change of the key node and the output of the new energy station, the influence of the output change of the new energy station on the voltage of the key node can be described quantitatively, and a foundation is provided for the subsequent establishment of a regulation and control mathematical model. Meanwhile, a search space distribution strategy is determined through historical output, compared with a standard marine predator algorithm, the method converts the disadvantage of irregular output change of the new energy station into advantages, so that the initial position of population individuals is more biased to high-density intervals of output in historical data, and the probability that the optimal output solution of the energy station is in the intervals is higher, therefore, the method jumps out of the application scene limitation of common uniform distribution initial positions, and can indirectly improve the search efficiency and the solving precision of the algorithm.
Preferably, the step S1: the method for acquiring the parameter information of the new energy station grid-connected system and determining the first-order and second-order sensitivity relationship between the key node power and the new energy station output comprises the following steps:
acquiring a tide equation of each key node based on parameter information of a new energy station grid-connected system;
solving a partial derivative about a voltage phase angle and a voltage for a tide equation to obtain first-order sensitivity;
solving a second-order partial derivative about a voltage phase angle and voltage for a tide equation to obtain second-order sensitivity;
and combining the first-order sensitivity and the second-order sensitivity to obtain a calculation formula of the active power and the reactive power of the first-order sensitivity and the second-order sensitivity of the key node caused by the power change of the new energy station, namely a sensitivity relation formula.
Preferably, the step S2: determining a relation function between voltage change of a key node and output of a new energy station according to the sensitivity relation and line parameters of the node, wherein the relation function comprises the following steps:
acquiring the impedance of a line where a node is located;
according to a circuit principle, determining a voltage and power relation of a key node based on a power flow equation of the key node and the impedance of a line where the key node is positioned;
and determining a relation function between the voltage change of the key node and the output of the new energy station based on the relation between the voltage and the power of the key node and the sensitivity relation.
Preferably, the constraint in S3 includes:
active power constraint of new energy station power generation equipment, reactive power constraint of reactive compensation device, voltage constraint of node and reactive output power constraint of new energy station.
Preferably, the step S4: determining a search space distribution strategy according to the historical output comprises the following steps:
acquiring historical output data and normalizing;
clustering the normalized historical output data to obtain a clustering center and population density;
determining segments of the search space based on the cluster centers;
the probability of selection of the search space is determined based on the population density.
Preferably, S5: based on the search space distribution strategy, the ocean predator algorithm is adjusted, and the adjustment and control mathematical model is optimized and calculated to obtain an optimal solution, comprising:
initializing marine predator algorithm parameters based on a relationship function in a regulatory mathematical model;
acquiring segmentation and selection probability of a search space according to a search space distribution strategy;
generating an initial position of the population based on the segmentation of the search space and the selection probability;
defining a mapping function for each segment;
for each individual, the initial position of the individual is adjusted by using a mapping function based on the segment where the individual is located, so that the individual is uniformly distributed in each segment;
iterating and updating predator positions by using a marine predator algorithm, and simultaneously solving the aggregation effect and the vortex effect of the fish shoal;
and outputting the obtained optimal solution until the individual comfort level reaches the optimal or the maximum iteration number.
In the invention, after the initial position is specially regulated according to the search space distribution strategy, individuals are uniformly distributed in each segment so as to balance the requirements of global search and local search, so that the local search can perform more comprehensive and detailed search in the found area, and the risk of the algorithm falling into a local optimal solution is reduced.
Preferably, the iterating and updating predator positions using a standard marine predator algorithm includes:
setting the maximum iteration number, and dividing the iteration into two stages based on the maximum iteration number;
performing a first stage iteration using a standard marine predator algorithm and updating predator positions;
if the individual comfort level reaches the optimal level, stopping iteration, otherwise, executing second-stage iteration;
in the second stage iteration, setting an adaptive adjustment factor of predator moving step length adjusted according to the iteration times;
and executing the second stage iteration until the individual comfort level reaches the optimal or the maximum iteration number, and outputting the obtained optimal solution.
Preferably, the dividing the iteration into two stages based on the maximum iteration number includes:
before iterating to two thirds of the maximum iterative times, the iterative stage is used as a first stage;
the remainder being the second stage.
The invention also provides a voltage support adjustment system of the new energy station grid-connected system, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor executes the voltage support adjustment method of the new energy station grid-connected system when calling the computer program in the memory.
The invention also provides electronic equipment, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the step of the voltage support adjustment method of the new energy station grid-connected system when calling the computer program in the memory.
The invention also provides a storage medium, wherein the storage medium stores computer executable instructions, and when the computer executable instructions are loaded and executed by a processor, the steps of the voltage support adjustment method of the new energy station grid-connected system are realized.
The essential effects of the invention include:
the invention firstly utilizes the sensitivity relation to quantify the influence of the output change of the new energy station on the voltage of the key node, and then constructs a regulating mathematical model aiming at minimizing the voltage change of the key node based on the influence. By introducing a search space distribution strategy and an optimized marine predator algorithm, the method can efficiently solve the regulation mathematical model and obtain an optimal solution, so as to determine an optimal new energy station output adjustment strategy.
Meanwhile, the search space distribution strategy is determined through the historical output force, compared with the direct use of a standard marine predator algorithm, the method converts the disadvantage of irregular output change of the new energy station into advantages, so that the initial position of the population individual is more biased to the high-density interval of output in the historical data, and the probability that the optimal solution of the output force of the energy station is positioned in the intervals is higher, therefore, the method jumps out of the application scene limitation of the common uniform distribution initial position, and can indirectly improve the search efficiency and the solving precision of the algorithm.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
FIG. 2 is a graph showing a partial node voltage change under a condition of small voltage drop in an embodiment of the present invention;
FIG. 3 is a graph showing the node voltage variation under the condition of small voltage drop in the embodiment of the present invention;
FIG. 4 is a graph showing a partial node voltage change under a condition of a larger voltage drop in an embodiment of the present invention;
FIG. 5 is a graph of time domain change of node voltage under a condition of larger voltage drop in an embodiment of the present invention;
FIG. 6 is a block diagram of an IEEE30 node system in accordance with an embodiment of the present invention;
FIG. 7 is a circuit diagram of a circuit pi-type equivalent circuit according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solution will be clearly and completely described in the following in conjunction with the embodiments, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be understood that, in various embodiments of the present invention, the sequence number of each process does not mean that the execution sequence of each process should be determined by its functions and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
It should be understood that in the present invention, "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements that are expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in the present invention, "plurality" means two or more. "and/or" is merely an association relationship describing an association object, and means that three relationships may exist, for example, and/or B may mean: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship. "comprising A, B and C", "comprising A, B, C" means that all three of A, B, C comprise, "comprising A, B or C" means that one of the three comprises A, B, C, and "comprising A, B and/or C" means that any 1 or any 2 or 3 of the three comprises A, B, C.
The technical scheme of the invention is described in detail below by specific examples. Embodiments may be combined with each other and the same or similar concepts or processes may not be described in detail in some embodiments.
Example 1: the voltage support adjustment method of the new energy station grid-connected system, as shown in fig. 1, comprises the following steps S1-S6, wherein:
s1: and acquiring parameter information of a new energy station grid-connected system, and determining a first-order and second-order sensitivity relationship between key node power and new energy station output.
Comprising the following steps: acquiring a tide equation of each key node based on parameter information of a new energy station grid-connected system;
solving a partial derivative about a voltage phase angle and a voltage for a tide equation to obtain first-order sensitivity;
solving a second-order partial derivative about a voltage phase angle and voltage for a tide equation to obtain second-order sensitivity;
and combining the first-order sensitivity and the second-order sensitivity to obtain a calculation formula of the active power and the reactive power of the first-order sensitivity and the second-order sensitivity of the key node caused by the power change of the new energy station, namely a sensitivity relation formula.
In this embodiment, the new energy station is a wind farm, and the wind farm uses a doubly-fed fan as an example, and the doubly-fed fan is generally controlled by a constant power factor, so that the doubly-fed fan is considered as a wind farm in researchNode, system line->Is->A type equivalent circuit as shown in fig. 7.
Wherein, the nodeThe flow equation is expressed as follows:
；
；
wherein:nodes +.>Active and reactive power of injection, +.>For node->Voltage of>、/>Nodes +.>Conductance between, susceptance, ->For node->Voltage phase angle difference between>Is the number of nodes.
For ease of representation, equation (1) is expressed in the form of an equation, written as follows:
；
wherein:for->、/>Vectors of (1), in particular->，
Expanding the formula (2) according to Taylor series, and neglecting higher-order terms three times or more to obtain the following formula:
；
wherein: j is a first-order sensitivity matrix, H is a second-order sensitivity matrix,
according to equation (3), if the second order partial derivative and the higher order term are ignored, the first order sensitivity, namely the load node, can be obtainedThe sensitivity of the voltage to the active force of node j is denoted +.>. If->The positive effect of increasing the active force of the node j on the voltage support of the load node i is shown; if->Increasing the active force at node j is shown to have a negative effect on the voltage support at load node i. The reactive first-order sensitivity matrix can be obtained, namely the sensitivity of the voltage of the load node i to the reactive output of each node j>. If->The reactive power output of the node j is improved to positively play a role in supporting the voltage of the load node i; if->It is shown that increasing the reactive power output of node j negatively affects the voltage support of load node i. However, the trend equation has obvious nonlinearity, if only the first-order sensitivity is considered, the calculation result is not accurate enough, so in the embodiment, the second-order sensitivity is considered at the same time to comprehensively analyze and calculate, and the influence of active and reactive changes of the new energy station on the system voltage support is considered.
Setting the active power and reactive power output of the new energy station as、/>Then, the equation (1 a) is obtained、/>Is expressed as follows:
；
；
the following written representation is given by formula (4):
；
further, the active power can be obtained by the formula (5)Output active power for new energy station>Reactive powerFirst order sensitivity +.>、/>The expression is as follows:
；
similarly, reactive power can be obtainedOutput active power for new energy station>No power->First order sensitivity +.>The expression is as follows:
；
from the formulae (6) and (7),and->Output change about new energy station->、/>First order sensitivity +.>The expression is as follows:
；
；
the expression of the formula (3) is required, and the power can be easily obtained from the formula (1)For->、/>And then the following relationship is obtained:
；
；
wherein:、/> 、/> 、/>respectively the active power about->、/>Is divided into a block matrix of second order partial derivatives,、/> 、/> 、/>reactive power in relation to->、/>Is divided into a block matrix by second order partial derivatives.
The newton method is calculated by means of the following flow:
；
wherein: j is the jacobian matrix of the power flow calculation,is a blocking matrix of matrix J, +.>。
Substituting formula (10) into formula (9), and changing the power injection of the new energy station、/>The resulting second order sensitivity is expressed as follows:
；
；
wherein: E. f is respectively matrix, specifically 。
According to formulas (8) and (11), a first-order and second-order sensitivity active power and reactive power calculation formula caused by the power change of the new energy station at the node i is obtained, wherein the calculation formula is as follows:
；
；
wherein:active and reactive power in initial state, respectively, +.>And->Is the power change of the new energy station>、/>Is a first order sensitivity of (a).
S2: and determining a relation function between the voltage change of the key node and the output of the new energy station according to the sensitivity relation and the line parameter of the node.
Comprising the following steps:
acquiring the impedance of a line where a node is located;
according to a circuit principle, determining a voltage and power relation of a key node based on a power flow equation of the key node and the impedance of a line where the key node is positioned;
and determining a relation function between the voltage change of the key node and the output of the new energy station based on the relation between the voltage and the power of the key node and the sensitivity relation.
Specifically, from the equation of circuit principle and power flow calculation, the node i voltage can be obtainedExpressed by the following formula:
；
wherein:for line->Impedance of->The active power and the reactive power of the node i are respectively.
From equations (12) and (13), the voltage change and the power change of the node i are obtained, and the first-order and second-order sensitivity calculation formulas caused by the power change of the new energy station are expressed as follows:
；
the relation expression of the voltage of the node i and the power change of the new energy station is obtained by the formula (14), the active and reactive output change of the new energy station can be regulated and controlled, the voltage change of the node i is regulated, and the voltage support of the new energy station to the power grid under the condition that the new energy station is connected to the power grid is realized. Based on the above, the invention aims at minimizing the voltage change of the key node of the system, substitutes the formula (14) into the following formula, and establishes the following relation function:
；
wherein: m is a key node set of a new energy station grid-connected system,the voltage at node i, as described by equation (14),is the rated voltage of node i.
S3: and setting constraint conditions, and establishing a regulating mathematical model of the voltage support of the key node by taking the minimum voltage change of the key node as a target based on the relation function.
Wherein the constraint conditions include:
active power constraint of new energy station power generation equipment, reactive power constraint of reactive compensation device, voltage constraint of node and reactive output power constraint of new energy station.
Active power emitted by the new energy station fan i meets the following constraint:
；
wherein:active power for fan i, +.>、/>The upper limit and the lower limit of active power are respectively sent out for the fan i.
Reactive power emitted by the fan i meets the following constraint:
；
wherein:reactive power for fan i, +.>、/>And respectively sending out upper and lower limits of reactive power for the fan i.
The reactive power compensation device k outputs reactive power, satisfying the following constraints:
；
wherein:reactive power for reactive power compensation device k, +.>、/>And respectively sending out upper and lower limits of reactive power for the reactive compensation device k.
The node i voltage satisfies the following constraint:
；
wherein:for node i voltage, ">、/>The upper and lower limits of the voltage at node i, respectively.
Reactive output power constraints. When reactive power is output, reactive power is output outwards from the station, and reactive power requirements of the station are met, so that when the new energy station i outputs reactive power to the power grid, the following constraint conditions are met:
；
wherein:is the minimum value of reactive power output of the ith new energy station,/for the new energy station>For the total reactive output power of the ith new energy station, < >>For the total reactive capacity of the ith new energy station, +.>、/>Reactive power of bus outlets in 2 adjacent periods of the ith new energy station, +.>、/>Bus outlet voltages in adjacent 2 periods, respectively, ">The bus outlet voltage is required in the operation of the power grid.
S4: and determining a search space distribution strategy according to the historical output.
Comprising the following steps:
acquiring historical output data and normalizing;
clustering the normalized historical output data to obtain a clustering center and population density;
determining segments of the search space based on the cluster centers;
the probability of selection of the search space is determined based on the population density.
In this embodiment, a K-means clustering algorithm is taken as an example. A number of clusters K is selected, where K can be determined by observing the distribution of the data, using Elbow Method (Elbow Method), or other cluster effectiveness index.
And carrying out cluster analysis on the historical output data by applying a K-means algorithm. Each cluster center represents a typical output pattern, and for ease of understanding, the present embodiment is illustrated with simplified clustering results.
For example, cluster 1: representing periods of low output, such as early morning and late night; clustering 2: representing periods of moderate exertion, such as early morning and evening; clustering 3: representing periods of high output such as noon and afternoon.
According to the clustering result, different distribution areas and densities are defined in the search space. For example: in the low-output region (cluster 1), population density may be lower, as the variation here may not be too large, not requiring too dense searching. In the medium output region (cluster 2), population densities are moderate to balance global and local searches. In the high output region (cluster 3), population density is higher, as there may be more opportunities for optimization and more complex output patterns.
S5: and adjusting the marine predator algorithm based on the search space distribution strategy, and performing optimization calculation on the control mathematical model to obtain an optimal solution.
Comprising the following steps: initializing marine predator algorithm parameters based on a relationship function in a regulatory mathematical model;
acquiring segmentation and selection probability of a search space according to a search space distribution strategy;
generating an initial position of the population based on the segmentation of the search space and the selection probability;
defining a mapping function for each segment;
for each individual, the initial position of the individual is adjusted by using a mapping function based on the segment where the individual is located, so that the individual is uniformly distributed in each segment;
iterating and updating predator positions by using a marine predator algorithm, and simultaneously solving the aggregation effect and the vortex effect of the fish shoal;
and outputting the obtained optimal solution until the individual comfort level reaches the optimal or the maximum iteration number.
In the invention, after the initial position is specially regulated according to the search space distribution strategy, individuals are uniformly distributed in each segment so as to balance the requirements of global search and local search, so that the local search can perform more comprehensive and detailed search in the found area, and the risk of the algorithm falling into a local optimal solution is reduced.
In this embodiment, iterating and updating predator positions using a standard marine predator algorithm includes:
setting the maximum iteration number, and dividing the iteration into two stages based on the maximum iteration number; before iterating to two thirds of the maximum iterative times, the iterative stage is used as a first stage; the remainder being the second stage.
Performing a first stage iteration using a standard marine predator algorithm and updating predator positions;
if the individual comfort level reaches the optimal level, stopping iteration, otherwise, executing second-stage iteration;
in the second stage iteration, setting an adaptive adjustment factor of predator moving step length adjusted according to the iteration times;
and executing the second stage iteration until the individual comfort level reaches the optimal or the maximum iteration number, and outputting the obtained optimal solution.
For ease of understanding, this embodiment first introduces a standard marine predator algorithm, namely:
initialization of MPA (Marine Predators Algorithm, marine predator algorithm):
MPA adopts a random search mode to initialize population in a space range, and the expression method is as follows:
；
wherein:coordinates in the j-th dimension for the ith individual in the population,/>、/>For the lower and upper bounds of the search space, rand is [0,1]Random numbers within.
The first stage: the iteration times at this stage areIn this case, the global search is particularly important when the predator is moving at a lower speed than the prey, which is similar to maintaining the current position. The location update is as follows:
；
wherein:for moving step size +.>Is a Brownian random vector in standard normal distribution,>for elite predator matrix, +.>For the prey matrix, p=0.5, r is [0,1]]Random vector in->Is the maximum number of iterations.
And a second stage: the number of iterations is atIn the method, the moving speed of predators and the moving speed of prey are approximately equal, at the moment, the population is divided into two parts, wherein the first part is that the prey performs development movement in a space range, and the second part is that the predators perform exploration movement in the space range. The position change of the development movement is as follows:
；
the position of the exploration movement varies as follows:
；
wherein:is a random vector in the Lewye distribution, +.>Is an adaptive parameter for predator movement step size.
And a third stage: the iteration times at this stage areIn, the predator moving speed is higher than the moving speed of the prey, the motion problem of the predator is converted into the position updating problem of the prey again through the random vector points of the Lewy multiplied by the elite predator matrix, and the position updating condition is as follows:
；
and (3) position updating: this stage is susceptible to the effects of shoal aggregation (Fish Aggregating Devices, FADs) and eddies, where local optima are jumped out by taking into account the longer transition of predators.
；
Wherein: fad=0.2, u is a binary vector containing only 0,1, and r is a random number uniformly distributed in [0,1 ].
While MPA has the advantages of strong local searching capability, rapid convergence speed in the later optimizing stage and the like, the early convergence speed of the algorithm is slower, and the local jumping-out capability is poorer, so that the embodiment jumps out of the standard use of the conventional application scene, and adjusts the marine predator algorithm based on the searching space distribution strategy, namely:
according to the search space distribution strategy, the segmentation and selection probability of the search space are obtained, for example, the search space is divided into three segments according to the clustering result, the probability of the third segment is highest, the probability of the second segment is moderate, and the probability of the first segment is lowest, so that the corresponding selection probability is set.
Generating initial positions of the population based on segmentation and selection probability of the search space, wherein the initial positions of the population obtained at the moment have the characteristic of uneven distribution and are biased to a high-density interval of output in historical data.
And, for each segment, defining a mapping function; for each individual, the initial position of the individual is adjusted by using a mapping function based on the segment where the individual is located, so that the individual is uniformly distributed in each segment;
；
wherein: c is a control parameter, and is used for controlling the control parameters,by assigning c->And (5) performing loop iteration to obtain a uniform random sequence.
At this time, the algorithm of the MPA in the initialization phase is updated as follows:
；
to increase the capacity of late jump out of MPA, the present embodiment also introduces adaptive adjustment factorsThe smaller self-adaptive adjustment factor is beneficial to accelerating the convergence speed and improving the global searching capability while the current searching region is more accurately subjected to local optimization.
；
Wherein:、/>an initial adaptive adjustment factor and a final adaptive adjustment factor, respectively.
At this time, the algorithm of the third stage of MPA is updated as follows:
；
using this algorithm, the objective function of equation (15) is optimized so that the value of the objective function is optimal.
In the embodiment, the first two of the three original phases of the marine predator algorithm are combined into one phase, so that the switching times of the algorithm in the iterative process can be reduced, the consumption of computing resources is reduced, and the execution efficiency of the algorithm is improved. And originally, the first two stages are mainly used for searching the prey in the global scope, and by combining the two stages, the searching capability of the algorithm in the global scope can be enhanced, so that the globally optimal solution is more likely to be found.
Meanwhile, since the initial position of the embodiment is adjusted by the search space distribution strategy, the risk of reducing the local development capability or increasing the risk of sinking into the local optimal solution, which may be caused by combining the first two stages, is also counteracted.
S6: and outputting a voltage support adjustment strategy according to the output of the new energy station corresponding to the optimal solution.
In this embodiment, by acquiring the parameter information of the system, the operation state and the characteristics of the system can be better understood, the sensitivity relationship between the power of the key node and the output of the new energy station is determined, the influence of the output change of the new energy station on the voltage of the key node can be known, and a decision basis is provided for the subsequent voltage support adjustment strategy. By establishing a relation function between the voltage change of the key node and the output of the new energy station, the influence of the output change of the new energy station on the voltage of the key node can be described quantitatively, and a foundation is provided for the subsequent establishment of a regulation and control mathematical model. Meanwhile, a search space distribution strategy is determined through historical output, compared with a standard marine predator algorithm, the method converts the disadvantage of irregular output change of the new energy station into advantages, so that the initial position of population individuals is more biased to high-density intervals of output in historical data, and the probability that the optimal output solution of the energy station is in the intervals is higher, therefore, the method jumps out of the application scene limitation of common uniform distribution initial positions, and can indirectly improve the search efficiency and the solving precision of the algorithm.
Example 2: the voltage support adjustment system of the new energy station grid-connected system comprises a memory and a processor, wherein a computer program is stored in the memory, and the processor executes the voltage support adjustment method of the new energy station grid-connected system when calling the computer program in the memory.
The present embodiment is applied to a new energy station of a doubly-fed wind turbine, and as shown in fig. 6, the calculation in this section takes an IEEE30 node system as an example, the system includes 41 branches and 6 generator nodes, and the new energy station is respectively connected to 6 nodes, 15 nodes and 22 nodes of the system.
In the calculation of the embodiment, equivalence processing is carried out on the new energy station containing the doubly-fed wind machine, reactive and active interaction exists between the new energy station and a connected power grid, and a voltage support task of key node voltage change is established.
According to the voltage change degree and the system operation condition, in order to better verify the voltage supporting effect of the embodiment, 2 groups of comparison schemes are set, and the operation conditions of the power grid are divided in the calculation: working condition 1. When the voltage drop amplitude is smaller; and 2. When the voltage drop amplitude is larger. Two modes are set under each working condition, and the first mode adopts the voltage supporting strategy provided by the embodiment to analyze the change condition of voltage; and secondly, analyzing the change condition of the voltage by adopting an adaptive voltage regulation method.
Aiming at the dynamic fluctuation process of the voltage, according to the voltage supporting method provided in the section 2, the voltage supporting capacities of the working condition 1 and the working condition 2 are analyzed.
1) Working condition 1: small voltage drop
The setting voltage of the working condition is reduced by a small margin, and the preset disturbance is realized: the load power increases slightly. The new energy station participates in the voltage support of the grid-connected system.
The system voltage is reduced, the voltage supporting strategy is adopted, the new energy stations participate in supporting node voltage, the three grid-connected new energy stations aim at the minimum deviation of the node voltage from the rated voltage according to the regulation and control task, the output of active power and reactive power is timely regulated, and part of node voltage changes are shown in figure 2. By adopting the self-adaptive voltage regulation method, partial node voltage change is shown in fig. 3.
As shown by comparing fig. 2 and fig. 3, the voltage rising speed is faster, the voltage can be recovered to the vicinity of the rated voltage in time, the voltage recovery in fig. 5 is slower, some nodes cannot be recovered to the rated voltage, and the voltage support of some nodes is obviously insufficient. This embodiment is shown to have a significant effect on voltage support.
2) Working condition 2: the voltage drop is large
The working condition is set as that the voltage is severely reduced, and disturbance is preset: the load power increases substantially. The new energy station participates in the voltage support of the grid-connected system.
The system voltage is greatly reduced, and the new energy station participates in node voltage support by adopting the voltage support strategy provided by the embodiment, and part of voltage change is shown in fig. 4. The voltage drop of the node is obvious, part of the nodes drop below 0.7, and the time domain change of the voltage of part of the nodes is shown in figure 5 by adopting an adaptive voltage regulation method.
The comparison between fig. 4 and fig. 5 shows that the voltage in fig. 4 is recovered to above 0.9, the voltage rising speed is faster in this embodiment, and the voltage rising speed can reach above 0.9 in time, but does not reach the vicinity of the rated voltage, which is mainly because under the condition that the voltage falling amplitude is larger, only the voltage support of the new energy station can not enable part of nodes to be completely recovered to the rated voltage to operate, and the side surface verifies the voltage support strategy of the new energy station to the system nodes, which is provided by this embodiment, and can exert good effects. While the voltage recovery of fig. 5 is slower, some nodes only recover to above 0.85, and the voltage support for some nodes is obviously insufficient. By comparison, the effect of this embodiment on node voltage support is very apparent.
Analysis and calculation results under different working conditions show that when the voltage of the nodes of the grid-connected system is reduced, the embodiment can achieve a good voltage supporting effect.
Example 3: an electronic device comprises a memory and a processor, wherein a computer program is stored in the memory, and the processor realizes the step of the voltage support adjustment method of the new energy station grid-connected system when calling the computer program in the memory.
Example 4: the storage medium stores computer executable instructions which, when loaded and executed by a processor, implement the steps of the new energy station grid-connected system voltage support adjustment method.
In summary, the essential effects of the present embodiment include:
firstly, the influence of the output change of the new energy station on the voltage of the key node is quantified by utilizing the sensitivity relation, and then a regulating mathematical model aiming at minimizing the voltage change of the key node is constructed on the basis. By introducing a search space distribution strategy and an optimized marine predator algorithm, the method can efficiently solve the regulation mathematical model and obtain an optimal solution, so as to determine an optimal new energy station output adjustment strategy.
Meanwhile, a search space distribution strategy is determined through historical output, compared with a standard marine predator algorithm, the method converts the disadvantage of irregular output change of the new energy station into advantages, so that the initial position of population individuals is more biased to high-density intervals of output in historical data, and the probability that the optimal output solution of the energy station is in the intervals is higher, therefore, the method jumps out of the application scene limitation of common uniform distribution initial positions, and can indirectly improve the search efficiency and the solving precision of the algorithm.
From the foregoing description of the embodiments, it will be appreciated by those skilled in the art that, for convenience and brevity of description, only the above-described division of functional modules is illustrated, and in practical application, the above-described functional allocation may be implemented by different functional modules according to needs, i.e. the internal structure of a specific apparatus is divided into different functional modules to implement all or part of the functions described above.
In the embodiments provided in this application, it should be understood that the disclosed structures and methods may be implemented in other ways. For example, the embodiments described above with respect to structures are merely illustrative, e.g., the division of modules or units is merely a logical functional division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another structure, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via interfaces, structures or units, which may be in electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and the parts shown as units may be one physical unit or a plurality of physical units, may be located in one place, or may be distributed in a plurality of different places. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a readable storage medium. Based on such understanding, the technical solution of the embodiments of the present application may be essentially or a part contributing to the prior art or all or part of the technical solution may be embodied in the form of a software product stored in a storage medium, including several instructions to cause a device (may be a single-chip microcomputer, a chip or the like) or a processor (processor) to perform all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read Only Memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (7)
1. The voltage support adjustment method for the grid-connected system of the new energy station is characterized by comprising the following steps of:
s1: acquiring parameter information of a new energy station grid-connected system, and determining a first-order and second-order sensitivity relationship between key node power and new energy station output;
s2: determining a relation function between the voltage change of the key node and the output of the new energy station according to the sensitivity relation and the line parameter of the node;
s3: setting constraint conditions, and establishing a regulating mathematical model of the voltage support of the key node by taking the minimum voltage change of the key node as a target based on the relation function;
s4: determining a search space distribution strategy according to the historical output;
s5: adjusting a marine predator algorithm based on a search space distribution strategy, and performing optimization calculation on a control mathematical model to obtain an optimal solution;
s6: outputting a voltage support adjustment strategy according to the output of the new energy station corresponding to the optimal solution;
the S2: determining a relation function between voltage change of a key node and output of a new energy station according to the sensitivity relation and line parameters of the node, wherein the relation function comprises the following steps:
acquiring the impedance of a line where a node is located;
according to a circuit principle, determining a voltage and power relation of a key node based on a power flow equation of the key node and the impedance of a line where the key node is positioned;
determining a relation function between the voltage change of the key node and the output of the new energy station based on the relation between the voltage and the power of the key node and the sensitivity relation;
the constraint conditions in S3 include:
active power constraint of new energy station power generation equipment, reactive power constraint of reactive compensation device, voltage constraint of node, reactive output power constraint of new energy station;
the S4: determining a search space distribution strategy according to the historical output comprises the following steps:
acquiring historical output data and normalizing;
clustering the normalized historical output data to obtain a clustering center and population density;
determining segments of the search space based on the cluster centers;
determining a selection probability of the search space based on the population density;
s5: based on the search space distribution strategy, the ocean predator algorithm is adjusted, and the adjustment and control mathematical model is optimized and calculated to obtain an optimal solution, comprising:
initializing marine predator algorithm parameters based on a relationship function in a regulatory mathematical model;
acquiring segmentation and selection probability of a search space according to a search space distribution strategy;
generating an initial position of the population based on the segmentation of the search space and the selection probability;
defining a mapping function for each segment;
for each individual, the initial position of the individual is adjusted by using a mapping function based on the segment where the individual is located, so that the individual is uniformly distributed in each segment;
iterating and updating predator positions by using a marine predator algorithm, and simultaneously solving the aggregation effect and the vortex effect of the fish shoal;
and outputting the obtained optimal solution until the individual comfort level reaches the optimal or the maximum iteration number.
2. The method for adjusting voltage support of a grid-connected system of a new energy station according to claim 1, wherein S1: the method for acquiring the parameter information of the new energy station grid-connected system and determining the first-order and second-order sensitivity relationship between the key node power and the new energy station output comprises the following steps:
acquiring a tide equation of each key node based on parameter information of a new energy station grid-connected system;
solving a partial derivative about a voltage phase angle and a voltage for a tide equation to obtain first-order sensitivity;
solving a second-order partial derivative about a voltage phase angle and voltage for a tide equation to obtain second-order sensitivity;
and combining the first-order sensitivity and the second-order sensitivity to obtain a calculation formula of the active power and the reactive power of the first-order sensitivity and the second-order sensitivity of the key node caused by the power change of the new energy station, namely a sensitivity relation formula.
3. The method for voltage support adjustment of a grid-connected system of a new energy farm according to claim 1, wherein the iterating and updating predator positions using a marine predator algorithm comprises:
setting the maximum iteration number, and dividing the iteration into two stages based on the maximum iteration number;
performing a first stage iteration using a standard marine predator algorithm and updating predator positions;
if the individual comfort level reaches the optimal level, stopping iteration, otherwise, executing second-stage iteration;
in the second stage iteration, setting an adaptive adjustment factor of predator moving step length adjusted according to the iteration times;
and executing the second stage iteration until the individual comfort level reaches the optimal or the maximum iteration number, and outputting the obtained optimal solution.
4. The method for adjusting voltage support of a grid-connected system of a new energy station according to claim 3, wherein the dividing the iteration into two stages based on the maximum number of iterations comprises:
before iterating to two thirds of the maximum iterative times, the iterative stage is used as a first stage;
the remainder being the second stage.
5. The voltage support adjustment system for the new energy station grid-connected system comprises a memory and a processor, and is characterized in that the memory stores a computer program, and the processor executes the voltage support adjustment method for the new energy station grid-connected system according to any one of claims 1-4 when calling the computer program in the memory.
6. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor, when calling the computer program in the memory, implements the steps of the new energy station grid-connected system voltage support adjustment method according to any one of claims 1 to 4.
7. A storage medium having stored therein computer executable instructions which, when loaded and executed by a processor, implement the steps of the new energy station grid-tie system voltage support adjustment method of any one of claims 1 to 4.
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