Substation dynamic reactive power compensation device grouping method

Abstract

The invention relates to a method for grouping dynamic reactive power compensation devices of a transformer substation, which is characterized in that an objective function is constructed with the lowest comprehensive cost after the dynamic reactive power compensation devices are grouped, and constraint conditions of the objective function comprise reactive power compensation demand constraint, capacity constraint of each group and reliability constraint; wherein when the reliability constraint considers any group of faults, it is ensured that the transformer substation operates at the safety level; a whale optimization algorithm is adopted to solve the target function; according to the method, the objective function is established through a mathematical method, multiple constraint conditions are considered, scientific and reasonable grouping of SVG can be realized, and the comprehensive cost is effectively reduced; the reliability constraint is considered, and the operation safety and stability of the transformer substation under the SVG fault condition are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of reactive power compensation in power systems, and particularly to a grouping method for substation dynamic reactive power compensation devices. Background Art

[0002] Under the background of large-scale access of new energy in the new power system, in order to strengthen the support of the grid adaptability technology system, the planned scale of substations needs to take into account the access requirements of new energy. The SVG dynamic reactive power compensation device is crucial for improving the safe and stable operation level of the grid connected with new energy, and it can realize smooth, continuous and bidirectional regulation of system reactive power.

[0003] For example, the "Optimal Reactive Power Planning Model for Distribution Networks Considering Fault Risk and Cost" disclosed in Patent Publication No. CN105244874B mainly focuses on the 10kV distribution network, optimizes the reactive power compensation capacity and location with the minimum network loss as the goal through the genetic algorithm, and selects the configuration capacity based on the whole life cycle management. This document does not study the problems caused by too many or too few SVG grouping numbers.

[0004] In the current optimization work of reactive power compensation devices, the common practice is to first determine the installation capacity of reactive power compensation devices based on cost calculation, and then group the reactive power compensation devices by methods such as equal-capacity grouping; however, there are many problems in current SVG grouping. When the number of grouping sets is too many, the capacity utilization of power electronic devices is insufficient, the unit cost is high, and the land occupation increases, resulting in an increase in civil engineering cost; when the number of grouping sets is too few, the reliability of the substation decreases; at the same time, if one group of SVG fails, it will have a greater impact on the substation.

[0005] Therefore, there is an urgent need for a scientific and reasonable SVG grouping method. Summary of the Invention

[0006] Aiming at the deficiencies of the prior art, the present invention provides a grouping method for substation dynamic reactive power compensation devices to solve the above problems.

[0007] The present invention provides the following technical solutions:

[0008] A grouping method for substation dynamic reactive power compensation devices includes the following steps:

[0009] Construct an objective function with the lowest comprehensive cost after grouping the dynamic reactive power compensation devices. The comprehensive cost includes equipment cost C1 and civil engineering cost C2, and the objective function C = C1 + C2;

[0010] The constraint conditions of the objective function include reactive power compensation demand constraint, capacity constraint for each group, and reliability constraint; among them, the reliability constraint considers that when any one group fails, the substation can operate at a safe level;

[0011] The whale optimization algorithm is used to solve the objective function.

[0012] Preferably, the objective function

[0013] where n is the number of groups of the dynamic reactive power compensation device, and S i is the capacity of each group of dynamic reactive power compensation devices; k i is the unit capacity equipment cost coefficient; k c is the civil engineering cost coefficient of each group.

[0014] Preferably, k i its value is obtained by statistical analysis of the prices of reactive power compensation devices on the market; k c its value is obtained through cost accounting of previous substation construction projects.

[0015] Preferably, the reactive power compensation demand constraint:

[0016] where S total is the total reactive power compensation capacity required by the substation.

[0017] 5. A method for grouping dynamic reactive power compensation devices of a substation according to claim 2, characterized in that the capacity constraint of each group:

[0018] S min ≤ S i ≤ S max ,S min and S max are the minimum and maximum values of the capacity of each group of dynamic reactive power compensation devices respectively.

[0019] Preferably, the reliability constraint:

[0020] When any one of the n groups fails, the remaining (n - 1) groups of dynamic reactive power compensation devices can also ensure the operation of the substation at a safe level, that is where S safe is the minimum reactive power compensation capacity to meet safe operation.

[0021] Preferably, the whale optimization algorithm includes the following steps:

[0022] Initialize the whale population: Randomly generate a certain number of whale individuals, and each individual represents a grouping scheme, that is, it contains information on the number of groups n and the capacity S of each group i of information;

[0023] Calculate the fitness value: According to the objective function and constraint conditions, calculate the fitness value of each whale individual. For individuals that do not meet the constraint conditions, a penalty value is given;

[0024] Update the position of the whales;

[0025] Judge the convergence condition: Judge whether the convergence condition is satisfied. If it is satisfied, output the optimal solution; if not, return to the step of calculating the fitness value and continue the iteration.

[0026] Preferably, the number N of the whale population is determined according to the empirical formula Determine;

[0027] where d is the total number of variables of the number of groups and the capacity of each group.

[0028] Preferably, the penalty value adopts the penalty function method;

[0029] For individuals that do not meet the reactive power compensation demand constraint, the penalty value is P1;

[0030]

[0031] For individuals that do not meet the capacity constraint of each group, the penalty value is P2;

[0032] P2 = M × (max(0, S min - S i ) + max(0, S i - S max ));

[0033] For individuals that do not meet the reliability constraint, the penalty value is P3;

[0034]

[0035] where M is a positive constant;

[0036] The fitness value F of the individual is F = C + P1 + P2 + P3. Determine the optimal individual in the current population and regard its position as the current optimal solution.

[0037] Preferably, updating the position of the whales includes:

[0038] Contraction and encirclement mechanism: Calculate the parameter a, which linearly decreases from 2 to 0 with the iteration number t. The formula is where T max is the maximum number of iterations; randomly generate vectors r1 and r2, and calculate the coefficient vector For each whale individual, if then the whale individual approaches the current optimal solution, and the update formula is If then the whale individual randomly searches within the search space, and the update formula is where X(t) is the current position of the whale individual, X * (t) is the position of the current optimal solution, X randis the position of a randomly selected whale individual.

[0039] Spiral motion mechanism: Calculate D ′ = |X * (t) - X(t)|, according to the formula X(t + 1) = X * (t) +

[0040] D ′ ·e bl cos(2πl) to update the position of the whale individual, where b is a constant controlling the spiral shape with a value of 1, and l is a random number between -1 and 1. The position of the whale is updated comprehensively through the shrinking encircling mechanism and the spiral motion mechanism to generate a new grouping scheme.

[0041] The present invention has the following beneficial technical effects:

[0042] The objective function constructed by the present invention is oriented towards the lowest comprehensive cost after grouping. The objective function comprehensively considers aspects such as equipment cost and civil construction cost. Through scientific and reasonable mathematical modeling, it fully considers the changes in various costs under different grouping schemes, and realizes the scientific and reasonable grouping of dynamic reactive power compensation devices.

[0043] Compared with traditional grouping methods, it effectively avoids problems such as insufficient utilization of the capacity of power electronic devices and increased civil construction cost caused by too many grouping groups, as well as increased frequent fault repair cost that may be brought about by too few grouping groups. Thus, it greatly reduces the comprehensive cost as a whole and improves the investment benefit.

[0044] When constructing the objective function, the reliability constraint conditions are fully considered, so that the substation can maintain a stable operating state during sudden failures. Different from the traditional scheme where too few grouping groups lead to reduced reliability, the grouping method of the present invention improves the redundancy and fault tolerance of the substation while ensuring the effective operation of the equipment. Even if a certain group of SVG fails, the impact on the substation can be minimized through the collaborative work of other groups.

[0045] Using the whale optimization algorithm to solve, it has better global search ability and convergence speed, can obtain the optimal grouping scheme more quickly and accurately, and improves the work efficiency. Description of the Drawings

[0046] Figure 1 is the flow chart of the present invention. Detailed Embodiments

[0047] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0048] Embodiment:

[0049] A method for grouping a substation dynamic reactive power compensation device is as Figure 1 shown:

[0050] Determine the objective function:

[0051] Taking the lowest comprehensive cost as the objective function, the comprehensive cost includes equipment cost and civil construction cost. Let the equipment cost be C1, which is related to the number of groups n of SVG (dynamic reactive power compensation device) and the capacity S of each group i and can be expressed as where k i is the unit capacity equipment cost coefficient, and its value can be obtained through statistical analysis of the prices of SVG equipment in the market. For example, after investigating the quotations of multiple equipment suppliers and combining the price trends of equipment with different capacity levels, it is determined that the k i of 35kV 10Mvar SVG is 80,000 yuan / Mvar, and the k i of 35kV 20Mvar SVG is 60,000 yuan / Mvar. The civil construction cost is C2, which is related to the number of groups n and can be expressed as C2 = k c n, where k c is the civil construction cost coefficient of each group. This coefficient takes into account the relevant civil engineering costs such as infrastructure construction, site leveling, and cable laying, and is obtained through cost accounting of previous substation construction projects. Then the objective function

[0052] Consider the constraint conditions:

[0053] Reactive power compensation demand constraint: where S total is the total reactive power compensation capacity required by the substation. The calculation of S total is based on the load characteristics of the power grid connected to the substation, the fluctuation range of new energy power generation, and the power grid stability requirements. By analyzing the historical load data of the power grid and combining the prediction model of new energy power generation, the total reactive power to be compensated by the substation under the most severe working conditions is determined.

[0054] Capacity constraint of each group: S min ≤S i ≤S max , S min and Smax They are the minimum and maximum values of the SVG capacity for each group respectively. S min The determination takes into account the minimum operable capacity of power electronic devices and the requirements for system control accuracy; S max is restricted by factors such as equipment manufacturing technology, heat dissipation conditions, and cost - effectiveness.

[0055] Reliability constraint: Set a reliability index R. When any one of the n groups fails, the remaining (n - 1) groups of SVG should be able to ensure the operation of the substation at a certain safety level, that is where S safe is the minimum reactive power compensation capacity to meet the safe operation. S safe The value of is determined according to the safety and stability standards of the power grid and the importance level of the substation.

[0056] The data sources include the historical operation data of the power grid, the technical parameters provided by equipment manufacturers, the engineering cost statistical data, etc.

[0057] Solution method: Use the whale optimization algorithm to solve the objective function;

[0058] Initialize the whale population: Randomly generate a certain number of whale individuals. Each individual represents a grouping scheme, that is, it contains the number of groups n and the capacity S of each group i The information. The number of whale population N can be determined according to the empirical formula (where d is the dimension of the problem. Here, d is the total number of variables of the number of groups n and the capacity S of each group i ) For example, if the total number of variables is 10, then is rounded up to 46 whale individuals. The number of groups n is randomly generated according to its value range (such as [2, 10]), and the capacity S of each group i is randomly generated within [S min , S max .

[0059] Calculate the fitness value: According to the objective function and constraint conditions, calculate the fitness value of each whale individual. For individuals that do not meet the constraint conditions, a large penalty value is given. The design of the penalty function uses the penalty function method. For example, for individuals that do not meet the reactive power compensation demand constraint, the penalty value is

[0060]

[0061] where M is a very large positive number, such as 10 6 ;

[0062] For individuals that do not meet the capacity constraint of each group, the penalty value is P2 = M×(max(0, S min - S i) + max(0, S i -S max ));

[0063] For individuals that do not meet the reliability constraints, the penalty value is

[0064] Then the fitness value F of the individual = C + P1 + P2 + P3. Determine the optimal individual in the current population and regard its position as the current optimal solution (prey position).

[0065] Update the whale position:

[0066] Contraction and enclosure mechanism: Calculate the parameter a, which linearly decreases from 2 to 0 with the iteration number t. The formula is where T max is the maximum number of iterations. Randomly generate vectors r1 and r2 (taking values between 0 and 1), and calculate the coefficient vector For each whale individual, if then the whale individual approaches the current optimal solution (prey position), and the update formula is If then the whale individual randomly searches within the search space, and the update formula is where X(t) is the current position of the whale individual, X * (t) is the position of the current optimal solution, X rand is the position of a randomly selected whale individual.

[0067] Spiral motion mechanism: Calculate D ′ = |X * (t) - X(t)|, and update the position of the whale individual according to the formula X(t + 1) = X * (t) + D ′ · e bl cos(2πl), where b is a constant controlling the spiral shape, taking the value of 1, and l is a random number between -1 and 1. Comprehensively update the whale position through the contraction and enclosure mechanism and the spiral motion mechanism to generate a new grouping scheme.

[0068] Judge the convergence condition: Judge whether the convergence condition is met, such as reaching the maximum number of iterations or the fitness value of the optimal solution changing very little in consecutive multiple iterations (such as less than a certain threshold ∈, let ∈ = 10 -6 etc. The maximum number of iterations T max is generally determined according to the complexity of the problem and experience. For example, set T max = 500. If it is satisfied, output the optimal solution; if not, return to the step of calculating the fitness value and continue the iteration.

[0069] The above-described embodiments merely represent specific embodiments of the present invention. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the patent for the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all fall within the protection scope of the present invention.

Claims

1. A method for grouping a dynamic reactive power compensation device in a substation, characterized in that, It includes the following steps: Construct an objective function with the lowest comprehensive cost after grouping the dynamic reactive power compensation devices. The comprehensive cost includes the equipment cost C1 and the civil construction cost C2, and the objective function C = C1 + C2; The constraint conditions of the objective function include reactive power compensation demand constraint, capacity constraint for each group, and reliability constraint; among them, the reliability constraint considers that when any one group fails, the substation operates at a safe level; Use the whale optimization algorithm to solve the objective function.

2. A method for grouping a substation dynamic reactive power compensation device according to claim 1, characterized in that Objective function Among them, n is the number of groups of the dynamic reactive power compensation device, and S i is the capacity of each group of dynamic reactive power compensation devices; k i is the equipment cost coefficient per unit capacity; k c is the civil engineering cost coefficient for each group.

3. A method for grouping a substation dynamic reactive power compensation device according to claim 2, characterized in that, k i Its value is obtained based on the statistical analysis of the prices of reactive power compensation devices in the market; k c Its value is obtained through the cost accounting of previous substation construction projects.

4. A method for grouping a substation dynamic reactive power compensation device according to claim 2, characterized in that, Reactive power compensation demand constraint: Among them, S total is the total reactive power compensation capacity required for the substation.

5. A method for grouping a substation dynamic reactive power compensation device according to claim 2, characterized in that, Capacity constraint for each group: S min ≤ S i ≤ S max , S min and S max are the minimum and maximum values of the capacity of each group of dynamic reactive power compensation devices, respectively.

6. A method for grouping a substation dynamic reactive power compensation device according to claim 2, characterized in that, Reliability constraint: When any one of the n groups fails, the remaining (n - 1) groups of dynamic reactive power compensation devices can also ensure the safe operation of the substation, that is where S safe is the minimum reactive power compensation capacity to meet the safe operation.

7. A method for grouping a substation dynamic reactive power compensation device according to claim 2, characterized in that, The whale optimization algorithm includes the following steps: Initialize the whale population: Randomly generate a certain number of whale individuals, where each individual represents a grouping scheme, that is, it contains the number of groups n and the capacity S of each group i information; Calculate the fitness value: According to the objective function and constraint conditions, calculate the fitness value of each whale individual. For individuals that do not meet the constraint conditions, give a penalty value; Update the whale position; Judge the convergence condition: Judge whether the convergence condition is met. If it is met, output the optimal solution; if not, return to the step of calculating the fitness value and continue the iteration.

8. A method for grouping a substation dynamic reactive power compensation device according to claim 7, characterized in that, The whale population N is determined according to the empirical formula as follows; Among them, d is the total number of variables of the number of groups and the capacity of each group.

9. A method for grouping a substation dynamic reactive power compensation device according to claim 7, characterized in that The penalty value adopts the penalty function method; For individuals that do not meet the reactive power compensation demand constraint, the penalty value is P1; For individuals that do not meet the capacity constraint for each group, the penalty value is P2; P2 = M × (max(0, S min - S i ) + max(0, S i - S max )); For individuals that do not meet the reliability constraint, the penalty value is P3; Among them, M is a positive constant; The fitness value F of the individual = C + P1 + P2 + P3, determine the optimal individual in the current population, and regard its position as the current optimal solution.

10. A method for grouping a substation dynamic reactive power compensation device according to claim 7, characterized in that, Updating the whale position includes: Contraction Encirclement Mechanism: Calculate parameter a, which linearly decreases from 2 to 0 with the iteration number t. The formula is where T max is the maximum number of iterations; randomly generate vectors r1 and r2, and calculate the coefficient vector For each whale individual, if then the whale individual approaches the current optimal solution, and the update formula is If then the whale individual randomly searches within the search space, and the update formula is where X(t) is the current position of the whale individual, X * (t) is the position of the current optimal solution, and X rand is the position of a randomly selected whale individual. Spiral motion mechanism: Calculate D′ = |X * (t) - X(t)|, and update the position of the whale individual according to the formula X(t + 1) = X * (t) + D′·e bl cos(2πl), where b is a constant controlling the spiral shape with a value of 1, and l is a random number between -1 and 1. Update the whale position comprehensively through the shrinking encircling mechanism and the spiral motion mechanism to generate a new grouping scheme.

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