CN114362133A - Power grid stability control method under homogenization condition - Google Patents

Abstract

The invention relates to the technical field of power distribution network bearing capacity evaluation, in particular to a power grid stability control method under a homogenization condition, which comprises the following steps: 1) based on the configuration rule of the minimum number of PMUs in the information network, a PMU optimization method for avoiding repeated schemes is adopted to define a model; 2) obtaining a power network topology inequality; 3) the transient stability problem of the intelligent power grid is described as a control task with the concentrated concentration and the consistent speed of a multi-agent group, and each agent is described according to a second-order dynamic model of the multi-agent; 4) defining a Lyapunov function as the total energy of the system; 5) and solving the power grid stability control. Compared with the prior art, the stability control method based on the linear variable parameter tracking method can effectively identify weak links influencing the stability of the operation of the power grid, provides a basis for the stability control of the alternating current-direct current hybrid system containing large-scale new energy, improves the stability and reliability of the system, and enhances the operation capability of the intelligent power grid.

Description

Power grid stability control method under homogenization condition

Technical Field

The invention relates to the technical field of power grid safety and stability control, in particular to a power grid stability control method under a homogenization condition.

Background

The ultra-high voltage alternating current and direct current transmission can realize large-scale, long-distance and large-capacity energy transmission and cross-regional asynchronous networking. With the increase of the proportion of the extra-high voltage alternating current and direct current power grid in the whole power system, the influence of the direct current power grid on the whole power transmission system is researched, and the rapid evaluation and weak link judgment of the reliability of the whole alternating current and direct current system are particularly important. In recent years, the attention of researchers to the safety, stability and stability evaluation of the extra-high voltage power grid is gradually promoted. At present, a stability evaluation model of a double-pulse ultrahigh-voltage direct-current power transmission system exists, but the influence of an alternating-current system is not considered comprehensively. Meanwhile, stability evaluation of the power network of all voltage levels under the extra-high voltage main grid frame is researched, but a comprehensive model is not established for the extra-high voltage alternating current and direct current system in the aspect of reliability evaluation indexes. At present, no mature and comprehensive index system and evaluation method exist for reliability evaluation of the extra-high voltage alternating current and direct current hybrid system.

From the analysis of the energy homogenization, in the process of converting kinetic energy in the alternating current-direct current hybrid system into transient potential energy, the power transmission elements on the cut sets of the alternating current-direct current hybrid system may bear more transient energy, so that the tearing phenomenon is generated. The cut set is the weakest link of the AC/DC hybrid system under a certain fault. Because the transient energy of the system is excessively concentrated after the fault, the alternating current and direct current hybrid system is unstable, and therefore the transient energy variation on the branch and the cut set can be used as a criterion for identifying the system instability. In the aspect of enhancing the operation capacity of the smart grid, the information network and the information flow play an important role.

In actual operation, the problems of excessive dependence of the system on information, overhead of communication and information processing and the like need to be considered: including redundant information and less relevant information and situations of poor performance due to an excessive computational burden. One strategy to improve the resiliency of smart grids is to determine the appropriate level of dependence on the information network and the information flow. The reasonable configuration of the information measurement equipment PMU on the quantity and the position achieves the goals of considerable overall situation, economy and reliability. The invention introduces the optimal configuration of PMU in the reliability promotion of the information network to determine the information dependence degree, which is a necessary means for the transient state stable real-time control of the system under the information-physical fusion network.

Disclosure of Invention

In order to solve the above problems, the present invention provides a power grid stability control method under the homogenization condition, and the technical scheme thereof is as follows:

the power grid stability control method under the homogenization condition comprises the following steps:

1) based on the configuration rule of the minimum number of PMUs in the information network, a PMU optimization method for avoiding repeated schemes is adopted to define a model;

2) obtaining a power network topology inequality;

3) the transient stability problem of the intelligent power grid is described as a control task with the concentrated concentration and the consistent speed of a multi-agent group, and each agent is described according to a second-order dynamic model of the multi-agent;

4) defining a Lyapunov function as the total energy of the system;

5) a power grid stability control solving method.

Defining a model in the step 1), wherein the expression is as follows:

in the formula, x_kIs 1 or 0 and indicates whether the physical grid node k has installed a PMU device.

Obtaining a power network topology inequality in the step 2), wherein the expression is as follows:

in the formula, G represents a set of all nodes connected to the node k.

Obtaining a power network topology inequality in the step 2), wherein the steps are as follows:

listing a node incidence matrix, wherein the expression is as follows:

wherein A ═ a_ij) For node incidence matrix, i, j two nodes are adjacent or equal a_ij1 otherwise a_ij0; 1 is a column vector whose elements are all 1;

the cost of installing PMU equipment at node i, the expression is as follows:

y_i＝1+0.1n_ch (1-4)

according to the constraint rule, the power network topology is quite considerable and available, and the expression is as follows:

in the formula (f)_k＝x_k+∑x_l；

To further illustrate the effect of the number of channels, the following variables are introduced for illustration, and the expression is as follows:

rewriting the above equation then yields the power network topology inequality.

Describing each agent in step 3), wherein the expression is as follows:

describing each agent in step 3), wherein the steps are as follows:

forward calculation, namely directly calculating a standard form correction equation of the alternating current system by adopting a sparse matrix solving algorithm to obtain a voltage correction quantity delta U of the kth iteration^(k)And

and (3) updating the k +1 step variable of the communication system, wherein the expression is as follows:

U^(k+1)＝U^(k)+ΔU^(k) (1-8)

back-substitution calculation, the voltage correction obtained by forward calculation

Substituting into the DC system standard form correction equation, combining the shift terms, and directly calculating the correction amount of the coordination variable of the k iteration

Sum DC variable correction

Updating the variable of the step k +1 of the direct current system, wherein the expression is as follows:

all variables in the system are obtained up to the DC system unbalance Δ D^(k)And the unbalance amount Delta F of the AC system^(k)Meeting the convergence criterion and ending the iteration;

defining a controller signal u_i＝P_L,jThe intelligent agent i is 1, …, and F corresponds to an intelligent agent containing a leading generator, because each partition only selects one leading generator, F also represents the number of partitions of the physical power grid; h is diag [ h ═ d₁…，h_i+1，h_i+1…h_Z]When i is less than or equal to F, h is 1, otherwise h is 0; and other auxiliary regulating agents in the same partition correspond to V ═ { F + i, …, Z }, and the auxiliary regulating power of the corresponding generator is P_F+i＝a_F+i·P_L,iAnd a represents that a proportionality coefficient L changing with the dominant generator power is an element of a physical relation matrix L, and the expression is as follows:

at this point a new control quantity u is introduced_iDefining a controller signal u_iComprises the following steps:

in the formula, the element B in the information relation matrix B_i≥(100*D_i)；

Let M_i＝D_i+B_iAnd determining a second-order system in the set containing the leading generator agent, wherein the expression is as follows:

in order to realize transient stability of the smart grid, distributed control signals are applied to the intelligent agents containing the leading generators in each partition, and the expression is as follows:

considering a second-order system consisting of expected values of the pilot feedback term in the kinetic equation, assuming initial velocity mismatch, the initial energy H₀The intelligent agent is a finite value, all the intelligent agents are gradually and consistently converged to corresponding speed reference quantity under the action of a control protocol, the expected value of the rotating speed of the generator is corresponded, and the globally stable group consistent behavior is finally realized, wherein the expression is as follows:

F_jk＝g_jkE_j[E_j-E_kcos(θ_j-θ_k)]-b_jkE_jE_ksin(θ_j-θ_k) (1-18)

P_i＝P_i0-K_Gi(f-f₀) (1-19)

in the formula, P_ej、Q_ejRespectively representing the active power and the reactive power of the node j; e_j、E_kRespectively represent the voltages of the nodes jk; g_jk、b_jkRespectively representing conductance and susceptance on a line; k_GiThe active power frequency static characteristic coefficient of the ith generator is represented; f represents a system frequency value; p_i0、f₀Representing the active power and the system frequency of the generator in the running state at the moment.

In step 4), defining a Lyapunov function as the total energy of the system, namely the total potential energy between the intelligent bodies and the sum of the relative potential energy and the kinetic energy between the physical quantity of the intelligent bodies and the expected reference quantity, wherein the expression is as follows:

in the step 4), defining a Lyapunov function as the total energy of the system, and the steps are as follows:

since the potential energy function V is symmetrical, the expression is as follows:

derivation can be found, the expression is as follows:

alternatively, the expression is as follows:

since L is a semi-positive definite Laplace matrix and c >0, L + cI is a positive definite matrix, therefore H <0, so the agent is asymptotically stabilized at the respective desired reference under the control input.

In the step 5), solving the power grid stability control, which comprises the following steps:

step 5.1) fault screening and classification, namely rapidly screening out the instability faults and identifying UM (uniform memory access) of the instability faults by using the quantification capacity of EEAC (energy efficiency evaluation), and classifying all the instability faults into the same UM fault subsets by using UM as a characteristic;

step 5.2) executing prevention control and emergency control coordination optimization of each same UM fault subset stability constraint respectively;

step 5.3) analyzing whether stability control conflicts exist among the sub-optimizations or not according to the topological relation and the specific control scene of each fault subset UM;

and 5.4) coordinating each sub-optimization until a convergence condition is met, and finally performing fault screening on the system for safety so as to ensure that the system does not have potential dangerous accidents after control optimization, as shown in FIG. 1.

Compared with the prior art, its beneficial effect lies in:

the stability control method based on the linear variable parameter tracking method can effectively identify weak links influencing the operation stability of the power grid, provides a basis for the stability control of the alternating current-direct current hybrid system containing large-scale new energy, improves the stability and reliability of the system, and enhances the operation capability of the intelligent power grid.

Drawings

FIG. 1 is a flow chart of a power grid stability control solution;

FIG. 2 is a graph of the relative rotor angle over time for each generator under a fault;

FIG. 3 is a graph of the change in rotational speed of each generator over time under a fault;

FIG. 4 is a graph of the relative rotor angle over time for each generator using a centralized control method;

FIG. 5 is a graph of the speed of each generator over time using a centralized control method;

FIG. 6 is a graph of the relative rotor angle over time for each generator using the bee-hive control method;

FIG. 7 illustrates the variation of the rotational speed of each generator over time after the use of the bee-hive control method;

fig. 8 illustrates the respective agent motion trajectories in the information space.

Detailed Description

In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings, which are illustrated in the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.

In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.

The solution of some embodiments of the invention is described below with reference to fig. 1-8.

An example analysis is carried out on an IEEE39 node smart grid model, wherein the model comprises 19 loads and 46 lines, and a MATLAB/Simulink platform is used for carrying out simulation research. The examples are divided into four scenes according to the classification of short-circuit faults, centralized control after the faults and bee-congestion control after the faults.

Example 1

Scene 1: assuming that t is 0s, a three-phase short-circuit fault occurs on the disturbed lines 21-22 of the power grid, and if t is 0.1s, the fault line is disconnected, the connection information and the controller of the physical network are not activated, and the communication quantity and the calculated quantity of the information network are minimum values. The relationship between the rotating speed of each generator and the rotor angle is shown in fig. 2 and fig. 3, the system operation is obviously unstable, the rotor angle tends to be enlarged continuously, and the power network has serious faults.

Example 2

Scene 2: in centralized control, each node of the power grid needs to transmit information to a dispatching center, and after centralized processing, the nodes receive control instructions of the dispatching center, and a large number of PMU devices and communication lines are needed to ensure information acquisition and transmission. The collection of the whole power grid state information is required to be completed in a period before centralized control after the fault is removed, and the collection and the processing of data can be completed within a preset critical time after the fault is processed under an assumed ideal condition. When t is 0.15s, all generator nodes are subjected to the differential-free regulation control. The rotor angle and the rotating speed of the generator change along with time under ideal conditions obtained by the method shown in the figures 4 and 5, and the system reaches a steady state in a short time. But the system is in a maximum information transfer and processing state. The control equipment of each generator node frequently utilizes an external energy device to adjust the power of the corresponding node, the overall energy consumption of the whole adjusting process is large, and the network topology structure and the performance of related equipment are not fully utilized.

Example 3

Scene 3: the stability performance of the adoption of the bee-hive control method is shown in fig. 6 and 7, distributed bee-hive control can reduce equipment cost, improve the situations of large processing capacity and large energy consumption of centralized control information, and is in a critical stability range within a 5s observation period, but the upper and lower interval ranges are large, and the time for stabilizing each intelligent agent of the smart grid after control is applied to each intelligent agent is long.

Example 4

Scene 4: the control performance using the method of the present invention is shown in fig. 8. The partition identification scheme of the smart grid is started within a preset critical time point after fault processing, so that the situation of larger damage is avoided, and the transient stability recovery capability of the smart grid is improved. The partition optimization control is applied to the intelligent agent containing the leading generator, the partitions of the intelligent power grid are determined firstly, the partitions are divided according to the similarity of the dynamic characteristic physical quantities describing the intelligent agents, the change of the physical quantities of the intelligent agents in the intelligent power grid corresponds to the movement trend of the individual in the information space, and the movement track of the individual carrying the state information shown in fig. 8 is described as an individual movement curve in a two-dimensional space. From the trend of the separation of the individuals in the information space, namely the change of the physical quantity of each intelligent agent after the fault, the fact that the G1 individual is obviously far away from other individuals and the G6 and G7 movement trends are closest can be known and obtained through the scheme of dynamic identification and partition of each intelligent agent.

Example 5

As shown in fig. 1, the power grid stability control solving process is as follows:

step a, a system ground state;

step b, screening and classifying faults;

c, respectively executing constraint prevention control and emergency control coordination optimization of each flux UM fault set;

d, judging whether stability control conflict exists among accident constraints, if not, ending, and outputting a stability control scheme; if yes, continuing to execute the step e;

step e, analyzing conflict reasons according to decoupling iteration and aggregation coordination, and obtaining a coordination method according to a specific scene;

and f, entering a global coordination optimizing algorithm.

Secondly, on the basis of information network reliability optimization, reasonable leading generators G1, G7 and G9 are respectively selected for each partition by combining actual active power output of each agent and node sensitivity weight arrangement of the agents, and distributed optimization control is applied to the agent containing the leading generator to achieve the aim of system transient stability. And finishing partition identification and leading generator selection within critical time after the fault is removed, and applying a distributed control scheme combining information-physical network data to the smart grid when t is 0.15 s. As shown in fig. 8, a time-dependent change curve of the state quantity of each agent is obtained, the agent in each partition achieves the control target, and the corresponding physical quantity converges to a certain value for each partition. The external energy injection can influence the actual operation to cause certain fluctuation, the observation shows that the power system is quickly recovered to a stable operation state, the rotating speed of the generator is stable and gradually approaches to the same state, the rotor angle meets the constraint requirement and tends to the stable state, a good effect is achieved under the conditions of small information use and external energy injection, the system recovery stabilization time is shorter, the range of the upper interval and the lower interval of the physical state quantity is smaller, and the stability margin of the system is increased.

As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Finally, it should be noted that: although the present invention has been described in detail with reference to the above embodiments, it should be understood by those skilled in the art that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.

Claims (6)

1. The power grid stability control method under the homogenization condition is characterized by comprising the following steps of:

1) based on the configuration rule of the minimum number of PMUs in the information network, a PMU optimization method for avoiding repeated schemes is adopted to define a model;

2) obtaining a power network topology inequality;

3) the transient stability problem of the intelligent power grid is described as a control task with the concentrated concentration and the consistent speed of a multi-agent group, and each agent is described according to a second-order dynamic model of the multi-agent;

4) defining a Lyapunov function as the total energy of the system;

5) and solving the power grid stability control.

2. The method for grid stability control under homogenization conditions according to claim 1, wherein:

defining the model in the step 1), wherein the expression is as follows:

in the formula, x_kIs 1 or 0, indicating whether the physical grid node k has installed a PMU device, f_k＝x_k+∑x_l，。

3. The method for grid stability control under homogenization conditions according to claim 1, wherein:

obtaining a power network topology inequality in the step 2), wherein the expression is as follows:

in the formula, G represents a set of all nodes connected to the node k.

4. The method for grid stability control under homogenization conditions according to claim 1, wherein:

describing each agent in the step 3), wherein the expression is as follows:

5. the method for grid stability control under homogenization conditions according to claim 1, wherein:

in step 4), the Lyapunov function is defined as the total energy of the system, namely the total potential energy between the intelligent bodies and the sum of the relative potential energy and the kinetic energy between the physical quantity of the intelligent bodies and the expected reference quantity, and the expression is as follows:

6. the method for grid stability control under homogenization conditions according to claim 1, wherein:

in the step 5), the power grid stability control is solved, and the steps are as follows:

step 5.1) fault screening and classification, namely rapidly screening out the instability faults and identifying UM (uniform memory access) of the instability faults by using the quantification capacity of EEAC (energy efficiency evaluation), and classifying all the instability faults into the same UM fault subsets by using UM as a characteristic;

step 5.2) executing prevention control and emergency control coordination optimization of each same UM fault subset stability constraint respectively;

step 5.3) analyzing whether stability control conflicts exist among the sub-optimizations or not according to the topological relation and the specific control scene of each fault subset UM;

and 5.4) coordinating each sub-optimization until a convergence condition is met.

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