CN112564106A - Power grid self-organization criticality adjusting method based on tidal current entropy - Google Patents

Power grid self-organization criticality adjusting method based on tidal current entropy Download PDF

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CN112564106A
CN112564106A CN202011469859.9A CN202011469859A CN112564106A CN 112564106 A CN112564106 A CN 112564106A CN 202011469859 A CN202011469859 A CN 202011469859A CN 112564106 A CN112564106 A CN 112564106A
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林子钊
刘雪飞
田启东
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Shenzhen Power Supply Bureau Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
    • H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]

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Abstract

The invention discloses a power grid self-organization criticality adjusting method based on a tidal current entropy, which comprises the following steps of: step S1, constructing a power grid operation load flow entropy used for representing the power system load flow balance degree according to the probability that the load rate of the power transmission line is in a set interval; step S2, constructing a power system power flow equilibrium degree model by taking the minimum power grid operation power flow entropy as an optimization target; and step S3, solving the power flow equilibrium model of the power system by taking the output of each unit of the power system as an optimization variable and taking the power flow equilibrium index of the power system as an optimal target on the basis of a particle swarm algorithm to obtain an optimal solution. The embodiment of the invention can describe the system flow and carry out optimization and adjustment, and improves the operation balance of the power grid to a certain extent, so that the system is far away from the self-organization critical state.

Description

Power grid self-organization criticality adjusting method based on tidal current entropy
Technical Field
The invention relates to the technical field of power grid control, in particular to a power grid self-organization criticality adjusting method based on a power flow entropy.
Background
The large-scale interconnection of the power grids improves the operation efficiency of the system and transmits electric energy from a power plant to users beyond thousands of miles on the one hand, and also increases the complexity and uncertainty of the power grids on the other hand, so that the disturbance spread range of the system is wider. In recent years, a plurality of power failure accidents of the interconnected power grid occur at home and abroad. The power system is an extensive dissipation system, and the system gradually evolves to reach a self-organization critical state. An important feature of the self-organizing critical state is that it exhibits a power-law behavior between the size of the fault occurring on the system and the corresponding cumulative probability.
In recent years, researchers have proposed a plurality of cascading failure models to simulate cascading failures of a power grid, and further research factors influencing propagation of the cascading failures of the power grid and critical behaviors of self-organization. At present, a concept of power grid power flow entropy is provided based on an entropy theory, the concept is used for describing the imbalance of line power flow distribution quantitatively, and then qualitative reflection of the imbalance on power grid cascading failure and self-organization criticality is researched. However, there is still a lack of sufficient research and coping methods for optimizing the operation mode of the system so as to make the system operate in a balanced manner and be far away from the critical state of self-organization.
Disclosure of Invention
The invention aims to solve the technical problem of providing a power grid self-organization criticality adjusting method based on a power flow entropy so as to optimize and adjust the power flow of a system, improve the running balance of the power grid to a certain extent and enable the system to be far away from a self-organization criticality.
In order to solve the technical problem, the invention provides a power grid self-organization criticality adjusting method based on a tidal current entropy, which comprises the following steps:
step S1, constructing a power grid operation load flow entropy used for representing the power system load flow balance degree according to the probability that the load rate of the power transmission line is in a set interval;
step S2, constructing a power system power flow equilibrium degree model by taking the minimum power grid operation power flow entropy as an optimization target;
and step S3, solving the power flow equilibrium model of the power system by taking the output of each unit of the power system as an optimization variable and taking the power flow equilibrium index of the power system as an optimal target on the basis of a particle swarm algorithm to obtain an optimal solution.
Further, in step S1, the maximum active transmission capacity of the transmission line i is set as
Figure BDA0002835884030000021
The active power transmitted by the transmission line i when the power grid operates is PliThen, the calculation method of the load factor of the power transmission line i is as follows:
Figure BDA0002835884030000022
wherein, i is 1,2,3, …, and N is the total number of transmission lines.
Further, the maximum active transmission capacity of the transmission line i
Figure BDA0002835884030000023
The constraints imposed include: thermal stability constraint, voltage loss limit constraint and static stability constraint are considered, and calculation is carried out
Figure BDA0002835884030000024
Taking the minimum value under three constraint conditions;
maximum active transmission capacity of power transmission line under three constraint conditions
Figure BDA0002835884030000025
The calculation formulas of (A) are respectively as follows:
maximum active transmission capacity under thermal stability constraints:
Figure BDA0002835884030000026
wherein, UNIs the rated voltage of the transmission line ImaxContinuously circulating the maximum current value for the power transmission line i;
considering the maximum active transmission capacity under the constraint of voltage loss limit:
Figure BDA0002835884030000027
wherein, Δ U is a voltage drop allowed by the transmission line i, and U is a voltage value of the transmission line i in actual operation and unit kV; q is the actual circulating reactive power of the transmission line i in Mvar; r and X are respectively the resistance and reactance of the power transmission line i in the unit omega;
c maximum active transmission capacity under static stability constraint:
Figure BDA0002835884030000028
wherein, U1、U2Respectively measuring voltage in unit kV at two ends of a line i of the power transmission line; x is the reactance value of the power transmission line i in the unit omega; delta is the voltage phase angle difference at two ends of the i line of the power transmission line, and unit rad.
Further, in step S1, the grid operation power flow entropy is:
H＝-C∑P(v)·lnP(v)
wherein C is a constant, and ln10 is taken;
p (V) is the load factor of the transmission line at (V)V,VV+1]E.g., the probability of V, V being a known constant sequence, V ═ V1,V2,...,Vn]：
Figure BDA0002835884030000031
Wherein lvFor the load factor of the transmission line in the interval (V)v,Vv+1]The number of lines in (1).
Further, in the step S2, with the minimum power grid operation power flow entropy as an optimization target, the power flow equilibrium degree model of the power system is constructed as follows: minH;
its equality constraint g (x, u) is:
Figure BDA0002835884030000032
the inequality constraint h (x, u) is:
Figure BDA0002835884030000033
Ui,min≤Ui≤Ui,max
|Pli|≤Pli max
h is the power grid operation load flow entropy, g (x, u) and H (x, u) are respectively the conventional equality constraint and inequality constraint conditions, and PGi、QGiActive and reactive power output of the generator respectively; pLi、QLiThe active and reactive magnitudes of the load are respectively; u shapeiIs the i node voltage magnitude; thetaijThe phase angle difference between the i node and the j node is obtained; gij、BijRespectively the line conductance and the susceptance; pGj,min、PGj,maxThe maximum active output and the minimum active output of the generator are respectively; qGj,min、QGj,maxThe maximum and minimum reactive output of the generator are respectively; u shapei,min、Ui,maxMinimum and maximum voltage values allowed for the i-node, respectively; pliThe active power transmitted by the line i when the power grid operates;
Figure BDA0002835884030000034
the maximum active transmission capacity for line i.
Further, the step S3 specifically includes:
step S31, initializing parameters of the power system and the particle swarm algorithm;
step S32, generating an initialization velocity matrix of each particle and a position matrix meeting constraint conditions;
step S33, calculating the adaptability value of each particle in the initial particle population representing the system under the output level of the unit to obtain the initial optimal value of the community, and calculating the individual extreme value pb of each particleestAnd global extremum gbestCarrying out initialization;
step S34, passing through individual extremum pbestAnd global extreme gbestUpdating the speed and position of each particle;
step S35, calculating each particle fitness function after the Nth generation of the particle population iteratively updates the position, and updating the optimal position of the particle population;
step S36, repeating the steps S34-S35 until the iteration condition is met, and finally obtaining the global extreme value gbestAs an optimization result of the unit output.
Further, the power system parameters include: the upper and lower output limits of the conventional unit, system load parameters and system network parameters; the particle swarm algorithm parameters comprise: particle population size NsizepNumber of iterations NintcrationAcceleration factor c1,c2Limit value v of particle velocitymax。
Further, the step S32 further includes:
generating an initial particle population of the unit output as NG×NsizepEach row of elements of the matrix is a particle and represents the output level of a system unit, and each particle meets the upper and lower limit constraints of the unit output and the power balance constraint of the system; n is a radical ofGThe number of the units.
Further, the step S34 is to update the speed and the position of each particle specifically as follows:
vid＝w*vid+c1r1(pid-xid)+c2r2(pgd-xid)
xid＝xid+vid
wherein v isid∈Vi,xid∈Xi,pgd∈gbest，XiIs the position vector of particle i, ViIs the velocity vector of particle i, i is 1,2, …, N, c1、c2Is a learning factor; r is1And r2Is [0,1 ]]A uniform random number within a range; w is a*Is a weight factor with a size satisfying w*+c1r1+c2r2＝1；vidIs the velocity of the particle, satisfies vid∈[-vmax,vmax]，vmaxIs a set constant.
Further, the step S35 is specifically defined asComprises the following steps: if the fitness value of the latest position of the particle is better than the individual optimal position pbestUpdating p according to the fitness value ofbestIs the latest position; if the N generation updates the position, the fitness value of the position optimal particle is smaller than the historical optimal initial position gbestG is updated according to the fitness value ofbestIs the position of the Nth generation optimal particle.
The embodiment of the invention has the beneficial effects that: the system flow can be described and optimized and adjusted, and the operation balance of the power grid is improved to a certain extent, so that the system is far away from a self-organization critical state.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flow diagram of a power grid self-organization criticality adjustment method based on a tidal current entropy according to an embodiment of the invention.
Fig. 2 is a schematic diagram illustrating that the maximum active transmission capacity of the transmission line is constrained according to the embodiment of the present invention.
Fig. 3 is a flowchart illustrating step S3 in accordance with an embodiment of the present invention.
Detailed Description
The following description of the embodiments refers to the accompanying drawings, which are included to illustrate specific embodiments in which the invention may be practiced.
Referring to fig. 1, an embodiment of the present invention provides a power grid self-organization criticality adjusting method based on a tidal current entropy, including:
step S1, constructing a power grid operation load flow entropy used for representing the power system load flow balance degree according to the probability that the load rate of the power transmission line is in a set interval;
step S2, constructing a power system power flow equilibrium degree model by taking the minimum power grid operation power flow entropy as an optimization target;
and step S3, solving the power flow equilibrium model of the power system by taking the output of each unit of the power system as an optimization variable and taking the power flow equilibrium index of the power system as an optimal target on the basis of a particle swarm algorithm to obtain an optimal solution.
Specifically, the imbalance of the line load rate is a main factor causing the modern power system to enter the critical state of self-organization, and therefore is also regarded as an important index for identifying the critical state of self-organization.
Let the maximum active transmission capacity of transmission line i be
Figure BDA0002835884030000051
The active power transmitted by the transmission line i when the power grid operates is PliThen, the load rate of the transmission line i can be expressed as:
Figure BDA0002835884030000052
wherein, i is 1,2,3, …, and N is the total number of transmission lines.
As shown in fig. 2, the maximum active transmission capacity of the transmission line
Figure BDA0002835884030000053
The main considerations are limited to three constraints: thermal stability constraints, voltage loss limit-of-concern constraints, and static stability constraints. Computing
Figure BDA0002835884030000054
Taking the minimum value under three constraint conditions.
Maximum active transmission capacity of power transmission line under three constraint conditions
Figure BDA0002835884030000055
The calculation formulas of (A) are respectively as follows:
a. maximum active transmission capacity under thermal stability constraint
Figure BDA0002835884030000061
Wherein, UNIs the rated voltage of the transmission line ImaxAnd continuously circulating the maximum current value for the power transmission line i.
b. Considering the maximum active transmission capacity under the constraint of voltage loss limit:
Figure BDA0002835884030000062
wherein, the delta U is the voltage drop allowed by the transmission line i, and is generally plus or minus 5 percent; u is the voltage value of the transmission line i in actual operation and is in unit kV; q is the actual circulating reactive power of the transmission line i in Mvar; and R and X are the resistance and the reactance of the power transmission line i respectively in the unit omega.
c. Maximum active transmission capacity under static stability constraints:
Figure BDA0002835884030000063
wherein, U1、U2Respectively measuring voltage in unit kV at two ends of a line i of the power transmission line; x is the reactance value of the power transmission line i in the unit omega; delta is the voltage phase angle difference at two ends of the i line of the power transmission line, and unit rad.
Setting a known constant sequence V ═ V1,V2,...,Vn]Then the load rate of the transmission line is at (V)V,VV+1]The probability P (V) for E V is:
Figure BDA0002835884030000064
wherein lvIndicates that the load factor of the transmission line is in the interval (V)v,Vv+1]The number of lines in (1).
The power grid operation flow entropy can be obtained according to the Shannon theorem and the formula as follows:
H＝-C∑P(v)·lnP(v) (6)
where C is a constant, ln10 is taken.
Step S2, the operation mode of the system is optimized by taking the minimum power grid operation flow entropy as an optimization target, namely:
minH (7)。
its equality constraint g (x, u) is:
Figure BDA0002835884030000071
the inequality constraint h (x, u) is:
Figure BDA0002835884030000072
Ui,min≤Ui≤Ui,max (10)
Figure BDA0002835884030000073
h is the power grid operation load flow entropy, g (x, u) and H (x, u) are respectively the conventional equality constraint and inequality constraint conditions, and PGi、QGiActive and reactive power output of the generator respectively; pLi、QLiThe active and reactive magnitudes of the load are respectively; u shapeiIs the i node voltage magnitude; thetaijThe phase angle difference between the i node and the j node is obtained; gij、BijRespectively the line conductance and the susceptance; pGj,min、PGj,maxThe maximum active output and the minimum active output of the generator are respectively; qGj,min、QGj,maxThe maximum and minimum reactive output of the generator are respectively; u shapei,min、Ui,maxMinimum and maximum voltage values allowed for the i-node, respectively; pliThe active power transmitted by the line i when the power grid operates;
Figure BDA0002835884030000074
the maximum active transmission capacity for line i.
Step S3 is to solve the optimal power flow balance degree model of the power system constructed according to the formula (7) by using a particle swarm algorithm. The algorithm adopts the forms of random particles, iterative optimization and the like to obtain the optimal solution, the convergence rate is high, and the implementation method is easy, so that the algorithm is applied to a plurality of fields. The basic idea of the particle swarm algorithm is as follows:
each possible solution that meets the constraints in the model is considered as a particle. Each particle has its own velocity and position, and the particle determines its position for the next iteration based on its current velocity and position. The particles fly in a solution space formed by model constraints according to a certain speed and direction, and the optimal solution is searched through the process. Each particle is assigned an adaptation value according to the value of the objective function set by the optimization model, which determines the quality of this particle. Each iteration process has an optimal particle, and each particle continuously searches iteration by searching the optimal particle in a solution space. Assuming that the optimization problem under study has D control variables, the search process is performed in a D-dimensional space. The size of the community is initialized to N, i.e. the community comprises N particles. Each particle in the community has its own current position and flying speed, and can be represented by a D-dimensional vector, such as a position vector X of a particle ii＝(xi1,xi2,…,xiD) I-1, 2, …, N, the velocity vector V of the particle ii＝(vi1,vi2,…,viD) I-1, 2, …, N, each particle has an individual extremum in the process of searching the optimal solution, representing the optimal solution that the particle has searched in the process of ending to the current iteration, and p is represented by a D-dimensional vectorbest＝(pi1,pi2,…,piD) I is 1,2, …, N. In addition to this, there is a global extremum gbest＝(pg1,pg2,…,pgD) Representing the best solution searched by the community as a whole.
The particle updates the current particle speed and position according to the found individual extreme value and global extreme value in the iterative process, as shown in formulas (12) and (13):
vid＝w*vid+c1r1(pid-xid)+c2r2(pgd-xid) (12)
xid＝xid+vid (13)
wherein v isid∈Vi,xid∈Xi,pgd∈gbest，c1、c2For learning factors, they can be obtained empirically, and are usually c1＝c2＝2,i＝1,2,…,N；r1And r2Is [0,1 ]]A uniform random number within a range; w is a*Is a weight factor with a size satisfying w*+c1r1+c2r2＝1；vidRepresenting the velocity of the particles within certain limits, e.g. vid∈[-vmax,vmax]，vmaxIs a set constant.
Further, in this embodiment, the solving of the optimal power flow balance degree model of the power system based on the particle swarm algorithm is an iterative calculation process with the output of each unit of the power system as an optimization variable and the optimal power flow balance degree index of the power system as a target. Let the system have NGSet of machines, each particle of the particle swarm algorithm represents a dimension NGThe vector of (1) and the fitness value of each particle are system power flow balance indexes under the output level of the particle representing unit, please refer to fig. 3, and a specific flow of solving the optimal power flow balance model of the power system by the particle swarm algorithm is as follows:
and step S31, initializing basic parameters, wherein the basic parameters to be prepared comprise system parameters and particle swarm algorithm parameters. The system parameters include: the upper and lower output limits of the conventional unit, system load parameters, system network parameters and the like. The particle swarm algorithm parameters comprise: particle population size NsizepNumber of iterations NintcrationAcceleration factor c1,c2Limit value v of particle velocitymaxAnd the like.
Step S32, an initial particle population and an initial velocity of the particle population are generated. Generating an initial population of particles of the unit output, i.e. NG×NsizepThe matrix of (a) is,each row of elements of the matrix is a particle and represents the output level of a system unit, so that each particle meets the upper and lower limit constraints of the output of the unit and the balance constraints of the system power; at the same time, randomly generating an initial velocity matrix of the particle population within a velocity limit range, which is also NG×NsizepOf the matrix of (a).
Step S33, calculating the power flow balance degree index of the system of each particle in the initial particle population under the output level of the representative unit, namely the fitness value of each particle according to the formula (6), obtaining the initial optimal value of the population, and calculating pb of each particleestAnd historical optimum gbestInitialization is performed.
Step S34, making N equal to 0; n is N +1, the velocity and position of each particle are updated according to equations (12) and (13).
And step S35, calculating each particle fitness function after the Nth generation of the particle population iteratively updates the position according to the formula (6), and updating the optimal position of the particle population. If the fitness value of the latest position of the particle is better than the optimal position p of the individualbestUpdating p according to the fitness value ofbestIs the latest position; if the N generation updates the position, the fitness value of the position optimal particle is smaller than the historical optimal initial position gbestG is updated according to the fitness value ofbestIs the position of the Nth generation optimal particle.
Step S36, repeating steps S34-S35 until N ═ NintcrationG obtained finallybestThe output of the unit is optimized.
As can be seen from the above description, the embodiments of the present invention have the following beneficial effects: the system flow can be described and optimized and adjusted, and the operation balance of the power grid is improved to a certain extent, so that the system is far away from a self-organization critical state.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present invention, and it is therefore to be understood that the invention is not limited by the scope of the appended claims.

Claims (10)

1. A power grid self-organization criticality adjusting method based on a tidal current entropy is characterized by comprising the following steps:
step S1, constructing a power grid operation load flow entropy used for representing the power system load flow balance degree according to the probability that the load rate of the power transmission line is in a set interval;
step S2, constructing a power system power flow equilibrium degree model by taking the minimum power grid operation power flow entropy as an optimization target;
and step S3, solving the power flow equilibrium model of the power system by taking the output of each unit of the power system as an optimization variable and taking the power flow equilibrium index of the power system as an optimal target on the basis of a particle swarm algorithm to obtain an optimal solution.
2. The method for adjusting power grid self-organization criticality based on power flow entropy of claim 1, wherein in the step S1, the maximum active transmission capacity of the power transmission line i is set to be Pli maxAnd the active power transmitted by the power transmission line i when the power grid operates is PliThen, the calculation method of the load factor of the power transmission line i is as follows:
Figure FDA0002835884020000011
wherein, i is 1,2,3, …, and N is the total number of transmission lines.
3. The method for adjusting power grid self-organization criticality based on power flow entropy as claimed in claim 2, wherein the maximum active transmission capacity P of the power transmission line ili maxThe constraints imposed include: thermal stability constraint, voltage loss limit constraint and static stability constraint are considered, and P is calculatedli maxTaking the minimum value under three constraint conditions;
maximum active transmission capacity P of power transmission line under three constraint conditionsli maxThe calculation formulas of (A) are respectively as follows:
maximum active transmission capacity under thermal stability constraints:
Figure FDA0002835884020000012
wherein, UNIs the rated voltage of the transmission line ImaxContinuously circulating the maximum current value for the power transmission line i;
considering the maximum active transmission capacity under the constraint of voltage loss limit:
Figure FDA0002835884020000013
wherein, Δ U is a voltage drop allowed by the transmission line i, and U is a voltage value of the transmission line i in actual operation and unit kV; q is the actual circulating reactive power of the transmission line i in Mvar; r and X are respectively the resistance and reactance of the power transmission line i in the unit omega;
c maximum active transmission capacity under static stability constraint:
Figure FDA0002835884020000021
wherein, U1、U2Respectively measuring voltage in unit kV at two ends of a line i of the power transmission line; x is the reactance value of the power transmission line i in the unit omega; delta is the voltage phase angle difference at two ends of the i line of the power transmission line, and unit rad.
4. The power grid self-organization criticality adjusting method based on power flow entropy as claimed in claim 3, wherein in the step S1, the power grid operation power flow entropy is as follows:
H＝-C∑P(v)·lnP(v)
wherein C is a constant, and ln10 is taken;
p (V) is the load factor of the transmission line at (V)V,VV+1]E.g., the probability of V, V being a known constant sequence, V ═ V1,V2,...,Vn]：
Figure FDA0002835884020000022
Wherein lvFor the load factor of the transmission line in the interval (V)v,Vv+1]The number of lines in (1).
5. The power grid self-organization criticality adjusting method based on the power flow entropy as claimed in claim 4, wherein the step S2 takes the minimum power flow entropy of the power grid operation as an optimization target, and the power flow equilibrium degree model of the power system is constructed by: minH;
its equality constraint g (x, u) is:
Figure FDA0002835884020000023
the inequality constraint h (x, u) is:
Figure FDA0002835884020000024
Ui,min≤Ui≤Ui,max
|Pli|≤Pli max
h is the power grid operation load flow entropy, g (x, u) and H (x, u) are respectively the conventional equality constraint and inequality constraint conditions, and PGi、QGiActive and reactive power output of the generator respectively; pLi、QLiThe active and reactive magnitudes of the load are respectively; u shapeiIs the i node voltage magnitude; thetaijThe phase angle difference between the i node and the j node is obtained; gij、BijRespectively the line conductance and the susceptance; pGj,min、PGj,maxThe maximum active output and the minimum active output of the generator are respectively; qGj,min、QGj,maxThe maximum and minimum reactive output of the generator are respectively; u shapei,min、Ui,maxMinimum and maximum voltage values allowed for the i-node, respectively; pliThe active power transmitted by the line i when the power grid operates; pli maxFor line i maxActive transmission capacity.
6. The power grid self-organization criticality adjusting method based on power flow entropy as claimed in claim 5, wherein the step S3 specifically includes:
step S31, initializing parameters of the power system and the particle swarm algorithm;
step S32, generating an initialization velocity matrix of each particle and a position matrix meeting constraint conditions;
step S33, calculating the adaptability value of each particle in the initial particle population representing the system under the output level of the unit to obtain the initial optimal value of the community, and carrying out individual extreme value p on each particlebestAnd global extremum gbestCarrying out initialization;
step S34, passing the individual extremum pbestAnd global extreme gbestUpdating the speed and position of each particle;
step S35, calculating each particle fitness function after the Nth generation of the particle population iteratively updates the position, and updating the optimal position of the particle population;
step S36, repeating the steps S34-S35 until the iteration condition is met, and finally obtaining the global extreme value gbestAs an optimization result of the unit output.
7. The power flow entropy-based grid self-organization criticality adjusting method according to claim 6, wherein the power system parameters comprise: the upper and lower output limits of the conventional unit, system load parameters and system network parameters; the particle swarm algorithm parameters comprise: particle population size NsizepNumber of iterations NintcrationAcceleration factor c1,c2Limit value v of particle velocitymax。
8. The power flow entropy-based grid self-organization criticality adjusting method according to claim 7, wherein the step S32 further comprises:
generating an initial particle population of the unit output as NG×NsizepOf (2) matrixRepresenting that each row of elements of the matrix is a particle and represents a system unit output level, and each particle meets unit output upper and lower limit constraints and system power balance constraints; n is a radical ofGThe number of the units.
9. The method for adjusting grid self-organization criticality based on power flow entropy of claim 8, wherein the step S34 is implemented by updating the speed and the position of each particle by:
vid＝w*vid+c1r1(pid-xid)+c2r2(pgd-xid)
xid＝xid+vid
wherein v isid∈Vi,xid∈Xi,pgd∈gbest，XiIs the position vector of particle i, ViIs the velocity vector of particle i, i is 1,2, …, N, c1、c2Is a learning factor; r is1And r2Is [0,1 ]]A uniform random number within a range; w is a*Is a weight factor with a size satisfying w*+c1r1+c2r2＝1；vidIs the velocity of the particle, satisfies vid∈[-vmax,vmax]，vmaxIs a set constant.
10. The power grid self-organization criticality adjusting method based on power flow entropy as claimed in claim 6, wherein the step S35 specifically includes: if the fitness value of the latest position of the particle is better than the optimal position p of the individualbestUpdating p according to the fitness value ofbestIs the latest position; if the N generation updates the position, the fitness value of the position optimal particle is smaller than the historical optimal initial position gbestG is updated according to the fitness value ofbestIs the position of the Nth generation optimal particle.
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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110707706A (en) * 2019-11-22 2020-01-17 燕山大学 Power transmission network planning method and system based on line power flow distribution

Patent Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110707706A (en) * 2019-11-22 2020-01-17 燕山大学 Power transmission network planning method and system based on line power flow distribution

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
李顺 等: "基于多指标效能分析的分布式潮流控制器选址定容优化策略", 《电力系统自动化》 *
桂建忠 等: "基于历史运行数据的电网运行状态演化特征提取", 《东北电力大学学报》 *
谢丽珠: "考虑车辆尾气排放因素的公交信号优先控制策略及微观仿真研究", 《工程科技Ⅱ辑》 *

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