Optimal configuration method of voltage sag control equipment considering reasonable hierarchical power supply

Abstract

The present invention relates to a method for optimizing the configuration of voltage sag control equipment considering reasonable hierarchical power supply, including the steps of determining demand, establishing a model, and solving the model. The method for optimizing the configuration of voltage sag control equipment considering reasonable hierarchical power supply provided by the present invention fully considers the actual operating status and demand of sensitive loads in the park, performs reasonable load power quality demand classification, and realizes differentiated optimization configuration in combination with a heuristic algorithm, which can effectively realize the optimization configuration of the voltage sag compensation device, and achieve the best economy while meeting the demand of sensitive loads and realizing reasonable hierarchical power supply.

Description

Technical Field

[0001] The present invention and a method for optimizing configuration of voltage sag management equipment considering reasonable hierarchical power supply belong to the technical field of electric power. Background Art

[0002] With the rapid development of high-tech industries represented by the semiconductor industry and the Internet industry, more and more power-sensitive devices are connected to the power grid, and the requirements for power quality are becoming more stringent. At the same time, the proportion of renewable energy connected to the power system will continue to increase, which will have a certain impact on power quality. Voltage sag, as one of the most serious power quality problems, has received more and more attention. Effective detection, evaluation and control of voltage sag phenomena to achieve highly reliable and high-quality power supply for power-sensitive users is not only of great economic significance, but also an inevitable trend in the development of the power system.

[0003] Voltage sag events not only occur frequently, but also bring huge economic and social losses. Voltage sag events can affect the normal operation of loads, especially for precision process control equipment such as computers, power electronics and automation technology. Therefore, it is not only of great economic significance but also an inevitable trend in the development of the power system to control voltage sag phenomena and achieve high-reliability and high-quality power supply for power-sensitive users.

[0004] At present, high-quality power parks provide differentiated levels of power supply to park users, which has attracted widespread attention while meeting the needs of different users and reducing power supply costs. When studying the configuration plan of voltage sag compensation equipment, most methods only consider the lowest cost of configuring equipment, but ignore the importance of reasonable classification of power supply levels. If the classification gap is too large, it will be difficult to meet the economic requirements, and if the classification gap is too small, it will lack practical feasibility. Summary of the invention

[0005] In order to overcome the problems in the prior art, the present invention proposes an optimal configuration method with the lowest configuration cost of DVR (Dynamic voltage restorer, which is a voltage sag management device composed of multiple DVRs) equipment on the basis of ensuring reasonable classification.

[0006] In order to solve the above technical problems, the technical solution proposed by the present invention is: a method for optimizing the configuration of voltage sag control equipment considering reasonable hierarchical power supply, comprising the following steps:

[0007] (1) Based on the raw data of sensitive equipment in the power park, determine the process capacity and voltage amplitude requirements of each device;

[0008] (2) Establish a DVR optimization configuration model. The objective function of the optimization configuration model is:

[0009]

[0010] In the formula, σ is the penalty factor, which takes a positive value; F DVR Represents the total cost of the configured DVR; N D is the number of DVRs configured; p si is the unit capacity cost of the i-th DVR; S i represents the capacity of the i-th DVR; t represents the voltage amplitude difference threshold of adjacent power supply levels, expressed in percentage; They represent the voltage demand amplitudes of the ith feeder and the kth feeder in the park respectively; represents the voltage demand threshold of the jth load connected to the ith feeder; represents the DVR output voltage corresponding to the i-th feeder; P j is the capacity of the jth load connected to the i-th feeder; J represents the total number of loads on the i-th feeder; U b represents the bus voltage; N represents the number of feeders;

[0011] (3) The improved grey wolf algorithm is used to solve the model; specifically:

[0012] Step (31): setting the initial parameters of the improved grey wolf algorithm, including population dimension, maximum number of iterations, coefficient vector and other parameters;

[0013] Step (32): Randomly generate an initial population and perform algorithm optimization; calculate the fitness of the wolf pack, determine and record the positions of the top three α wolves, β wolves, and δ wolves, denoted as X α , X β , X δ ;

[0014] Step (33): Update the convergence factor s of the improved grey wolf algorithm. The formula is: In the formula, T represents the number of iterations of the gray wolf algorithm;

[0015] Step (34): Update the position of the wolf pack. The specific formula is:

[0016] D=|C·X p (t)-X(t)|;

[0017] X(t+1)=X p (t)-A·D;

[0018] In the formula, D represents the distance between the individual wolf and the hunting target, X(t+1) represents the updated position of the wolf pack, A and C represent coefficient vectors, X is the position vector of the wolf, and Xp is the position vector of the hunting target; the solution formula for the coefficient vector is:

[0019]

[0020] In the above formula, n1 and n2 are random values ​​between 0 and 1;

[0021] Step (35): Update the position of the wolf pack;

[0022]

[0023]

[0024] Where D α , D β , D δ Respectively represent the distances between α wolf, β wolf, δ wolf and the hunting target, X1, X2, X3 represent the updated position vectors of α wolf, β wolf, δ wolf respectively;

[0025] Then through the formula: Determine the updated position of the current wolf;

[0026] Step (36): Return to step (32) to calculate the updated fitness, and re-determine α wolf, β wolf, and δ wolf;

[0027] Step (37): Determine whether the algorithm has reached the maximum number of convergences: if it has, it is considered that the optimal solution has been determined, and the next step is performed; if it has not, return to step (32) to continue iterating to find the optimal solution;

[0028] Step (4): The DVR optimization configuration result in the output result is the final DVR device optimization configuration plan that takes reasonable classification into consideration.

[0029] The above scheme is further improved in that: the improved grey wolf algorithm in step (3) performs nonlinear processing on the linear convergence factor in the traditional algorithm, making the hunting behavior of the wolf pack more comprehensive and reasonable.

[0030] The method for optimizing the configuration of voltage sag control equipment taking into account reasonable hierarchical power supply provided by the present invention fully considers the actual operating status and requirements of the sensitive loads in the park, performs reasonable load power quality demand classification, and combines the heuristic algorithm to achieve differentiated optimization configuration. It can effectively realize the optimal configuration of the voltage sag compensation device, while meeting the needs of sensitive loads and realizing reasonable hierarchical power supply, achieving the best economy. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The present invention will be further described below in conjunction with the accompanying drawings.

[0032] Figure 1This is a table of specific industrial links, process capacity and voltage amplitude requirements of a precision temperature control system of an optical center in a high-quality power park in a certain place, according to a preferred embodiment of the present invention.

[0033] Figure 2 It is a table of ordinary, good and high-quality classification results when considering reasonable classification of power supply.

[0034] Figure 3 This is a table of ordinary, good and high-quality grading results without considering reasonable grading of power supply.

[0035] Figure 4 This is a comparison table of the results of different configuration solutions. DETAILED DESCRIPTION

[0036] Example

[0037] The method for optimizing configuration of voltage sag control equipment considering reasonable hierarchical power supply provided in this embodiment includes the following steps:

[0038] Step (1) determines the process capacity and voltage amplitude requirements of each device based on the original data of sensitive devices in the power park. In this embodiment, a precision temperature control system from an optical center in a high-quality power park in a certain place is taken as an example. The specific industrial links, process capacity and voltage amplitude requirements of the system are as follows: Figure 1 As shown in the table.

[0039] Step (2) establishes a DVR optimization configuration model, including an objective function and constraints; the objective function of the optimization configuration model is:

[0040]

[0041] In the formula, σ is the penalty factor, which takes a positive value; F DVR Represents the total cost of the configured DVR; N D is the number of DVRs configured; p si is the unit capacity cost of the i-th DVR; S i represents the capacity of the i-th DVR; t represents the voltage amplitude difference threshold of adjacent power supply levels, expressed in percentage; They represent the voltage demand amplitudes of the ith feeder and the kth feeder in the park respectively; represents the voltage demand threshold of the jth load connected to the ith feeder; represents the DVR output voltage corresponding to the i-th feeder; P j is the capacity of the jth load connected to the i-th feeder; J represents the total number of loads on the i-th feeder; U b represents the bus voltage; N represents the number of feeders; to achieve hierarchical power supply.

[0042] In this embodiment, the threshold difference of each voltage amplitude of the hierarchical power supply is set to at least 15%Un, and the optimization configuration is performed with the goal of minimizing the total cost of the configured DVRs. The process capacity and voltage amplitude requirements of each device are as follows: Figure 1 As shown in the table; considering the most serious voltage sag, that is, short-term voltage interruption, the bus voltage is 0 at this time, and the value of k in formula (1) is 0; in this embodiment, the voltage level is divided into three levels (excellent, good, and ordinary).

[0043] Step (3) uses the improved grey wolf algorithm to solve the model, specifically:

[0044] Step (31) sets the initial parameters of the improved grey wolf algorithm, including parameters such as population dimension, maximum number of iterations, and coefficient vector;

[0045] Step (32) randomly generates an initial population and performs algorithm optimization; calculates the fitness of the wolf pack, determines and records the positions of the top three α wolves, β wolves, and δ wolves, and records them as X α , X β , X δ ;

[0046] Step (33) updates the convergence factor of the improved grey wolf algorithm, and the formula is:

[0047]

[0048] In the above formula, T represents the number of iterations of the Grey Wolf Algorithm.

[0049] Step (34) updates the wolf pack position. The specific formula is:

[0050] D=|C·X p (t)-X(t)| (3)

[0051] X(t+1)=X p (t)-A·D (4)

[0052] In formula (2), D represents the distance between the individual wolf and the target, formula (3) represents the updated position of the wolf pack, where A and C represent coefficient vectors, X is the position vector of the wolf, and Xp is the position vector of the target. The solution formula for the coefficient vector is:

[0053]

[0054] In the above formula, n1 and n2 are random values ​​between 0 and 1; s is the convergence factor mentioned in step (33); step (35) updates the position of the wolf pack;

[0055]

[0056]

[0057]

[0058] Formula (6) represents the distance between wolf α, wolf β, wolf δ and the hunting target, respectively. Formula (7) represents the updated position vectors of wolf α, wolf β, wolf δ, respectively. The updated position of the current wolf is determined by the position vectors of the three wolves and formula (8).

[0059] Step (36) returns to step (32) to calculate the updated fitness and re-determine α, β, and δ wolves;

[0060] Step (37) determines whether the algorithm has reached the maximum number of convergences: if it has, it is considered that the optimal solution has been determined, and the next step is performed; if it has not, it returns to step (32) to continue iterating to find the optimal solution;

[0061] The DVR optimization configuration result in the output of step (38) is the final DVR device optimization configuration plan that takes reasonable classification into consideration.

[0062] In this embodiment, the population size of the improved grey wolf algorithm is set to 50, and the maximum number of iterations is set to 100.

[0063] The calculation results show that the power supply of the precision temperature control system is classified into three levels (normal, good, and high-quality). Considering the reasonable classification of power supply, the DVR optimization configuration results are as follows: Figure 2 As shown in the table.

[0064] Depend on Figure 2 The configuration results in the table show that the DVR configuration scheme for the precision temperature control system with three levels of power supply is as follows: the voltage amplitude threshold of the ordinary power supply level is 53% Un, and a 33.39kVA DVR device is configured; the voltage amplitude threshold of the good level is 74% Un, and the configuration capacity is 214.896kVA; the threshold of the high-quality power supply level is 90% Un, and a 153.8kVA DVR device needs to be configured. The gap between the voltage levels under this scheme is at least 16% Un, and the classification is reasonable and feasible. The total cost of the DVR configuration of this scheme is 358,797.07 yuan.

[0065] If the DVR is configured without considering the reasonable hierarchical power supply, the optimized configuration is divided into three power supply levels. The optimized configuration results are as follows: Figure 3 As shown in the table.

[0066] Depend on Figure 3From the table data, we can see that if the DVR is optimized without considering the rationality of the classification, the power supply voltage thresholds are 53%Un, 80%Un and 90%Un. Although the cost of the DVR configured at this time is 14199.44 yuan lower than that when the reasonable classification is considered, the difference between the high-quality and good voltage levels is only 10%Un. At this time, the interval gap between the power supply levels is too small, which lacks guiding significance and feasibility in practical applications.

[0067] Comparing the configuration results of the example with the DVR power supply alone, we can get the following: Figure 4 A summary of the configuration options is shown in the table.

[0068] Depend on Figure 4 As shown in the table, if only a single DVR is used to power sensitive loads of all industrial processes, the capacity of the DVR needs to be configured is much higher than the total capacity of the DVR configuration under other schemes, and the configuration cost reaches 445,680 yuan, which is much higher than the configuration scheme under other tiered power supply, and lacks economy; if the rationality of the grading is not considered, although the cost is lower than that of considering reasonable grading, the gap between the high-quality grade and the good grade is too small, which lacks guidance and feasibility in practical applications.

[0069] Therefore, taking into account the rationality of the power supply grade classification interval and the economy of the DVR configuration, the precision temperature control system is divided into three levels for power supply. The normal, good and high-quality power supply voltage thresholds are 53% Un, 74% Un and 90% Un respectively. At this time, the total cost of the DVR configuration is 358,797.07 yuan, achieving the unity of economy and classification rationality.

[0070] The present invention is not limited to the above embodiments, and any technical solutions formed by equivalent replacement shall fall within the protection scope required by the present invention.

Claims

1. A method for optimizing the configuration of voltage sag control equipment considering reasonable hierarchical power supply, characterized in that: The steps include: (1) Based on the raw data of sensitive equipment in the power park, determine the process capacity and voltage amplitude requirements of each device; (2) Establish a DVR optimization configuration model. The objective function of the optimization configuration model is: In the formula, σ is the penalty factor, which takes a positive value; F DVR Represents the total cost of the configured DVR; N D is the number of DVRs configured; p si is the unit capacity cost of the i-th DVR; S i represents the capacity of the i-th DVR; t represents the voltage amplitude difference threshold of adjacent power supply levels, expressed in percentage; They represent the voltage demand amplitudes of the ith feeder and the kth feeder in the park respectively; represents the voltage demand threshold of the jth load connected to the ith feeder; represents the DVR output voltage corresponding to the i-th feeder; P j is the capacity of the jth load connected to the i-th feeder; J represents the total number of loads on the i-th feeder; U b represents the bus voltage; N represents the number of feeders; (3) The improved grey wolf algorithm is used to solve the model; specifically: Step (31): setting the initial parameters of the improved grey wolf algorithm, including population dimension, maximum number of iterations and coefficient vector; Step (32): Randomly generate an initial population and perform algorithm optimization; calculate the fitness of the wolf pack, determine and record the positions of the top three α wolves, β wolves, and δ wolves, denoted as X α , X β , X δ ; Step (33): Update the convergence factor s of the improved grey wolf algorithm. The formula is: In the formula, T represents the number of iterations of the gray wolf algorithm; Step (34): Update the position of the wolf pack. The specific formula is: D=|C·X p (t)-X(t)|; X(t+1)=X p (t)-A·D; In the formula, D represents the distance between the individual wolf and the hunting target, X(t+1) represents the updated position of the wolf pack, A and C represent coefficient vectors, X is the position vector of the wolf, and Xp is the position vector of the hunting target; the solution formula for the coefficient vector is: In the above formula, n1 and n2 are random values ​​between 0 and 1; Step (35): Update the position of the wolf pack; Where D α , D β , D δ Respectively represent the distances between α wolf, β wolf, δ wolf and the hunting target, X1, X2, X3 represent the updated position vectors of α wolf, β wolf, δ wolf respectively; Then through the formula: Determine the updated position of the current wolf; Step (36): Return to step (32) to calculate the updated fitness, and re-determine α wolf, β wolf, and δ wolf; Step (37): Determine whether the algorithm has reached the maximum number of convergences: if it has, it is considered that the optimal solution has been determined, and the next step is performed; if it has not, return to step (32) to continue iterating to find the optimal solution; Step (4): The DVR optimization configuration result in the output result is the final DVR device optimization configuration plan that takes reasonable classification into consideration.

2. The method for optimizing configuration of voltage sag management equipment considering reasonable hierarchical power supply according to claim 1 is characterized in that: The improved grey wolf algorithm in step (3) performs nonlinear processing on the linear convergence factor in the traditional algorithm.

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