CN113517723A - Reactive voltage optimization method for power distribution network system comprising small hydropower station - Google Patents

Reactive voltage optimization method for power distribution network system comprising small hydropower station Download PDF

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CN113517723A
CN113517723A CN202110781640.0A CN202110781640A CN113517723A CN 113517723 A CN113517723 A CN 113517723A CN 202110781640 A CN202110781640 A CN 202110781640A CN 113517723 A CN113517723 A CN 113517723A
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和鹏
覃日升
王加富
郭成
张艳萍
徐志
段锐敏
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Electric Power Research Institute of Yunnan Power Grid Co Ltd
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Abstract

The application provides a reactive voltage optimization method of a power distribution network system comprising a small hydropower station, which comprises the following steps: calculating the sensitivity of each node in a power distribution network system containing the small hydropower station; comparing the sensitivity of each node, and selecting the node with the sensitivity higher than a sensitivity threshold value as a reactive compensation candidate configuration point; establishing a reactive power optimization model of the power distribution network, and taking the minimum apparent power network loss of the power system as a target function; solving the reactive power optimization model of the power distribution network by utilizing a particle swarm-genetic fusion algorithm to obtain the optimal reactive power compensation capacity; and adjusting the reactive power compensation quantity of the power distribution network system containing the small hydropower station through a reverse equivalent pairing principle, and performing network loss compensation on the power distribution network system. The minimum active power network loss is calculated through a particle swarm-genetic fusion algorithm, the voltage distribution of the power distribution network is improved, and the loss of a power system can be effectively reduced.

Description

Reactive voltage optimization method for power distribution network system comprising small hydropower station
Technical Field
The application relates to the technical field of loss reduction of a power grid, in particular to a reactive voltage optimization method of a power distribution network system comprising a small hydropower station.
Background
With the rapid development of national economy and the increase of electricity consumption, the economic operation of the power grid is increasingly emphasized; and the reactive power optimization of the power distribution system is just an effective means for ensuring the safe and economic operation of the system. The reactive power reasonable optimization can not only improve the voltage level of the system operation, but also reduce the active power network loss and the reactive power network loss of the system and improve the operation efficiency of the power system.
The method provides solutions such as particle swarm optimization, genetic algorithm and the like for the problems of combination optimization of the power system unit and reactive power optimization of the power distribution network system. Particle Swarm Optimization (PSO) is a heuristic algorithm based on Swarm intelligence. The method has the advantages of high convergence speed, simple calculation and strong universality, and can be conveniently used for solving the complex optimization problems of nonlinearity, discontinuity, multiple constraints and multiple variables with discrete variables. However, the particle swarm algorithm has a problem that local convergence is easily caused. Genetic Algorithm (GA) is a computational model of the biological evolution process that simulates the natural selection and Genetic mechanism of darwinian biological evolution theory, and is a method for searching for an optimal solution by simulating the natural evolution process. The method is mainly characterized in that the method directly operates the structural object without the limitation of derivation and function continuity; the method has the advantages of inherent hidden parallelism and better global optimization capability; by adopting a probabilistic optimization method, the search direction can be self-adaptively adjusted. However, because a single genetic algorithm code cannot comprehensively express the constraint of the optimization problem, the threshold value adopted for the infeasible solution needs to be considered, and further the workload and the solving time are increased, so that the efficiency of the genetic algorithm is generally lower than that of other traditional optimization methods, and the genetic algorithm also has the problem of premature convergence.
The reactive power optimization of the power system is a multivariable, multi-constraint and non-continuity mixed nonlinear programming problem, so that how to comprehensively design an optimization algorithm to solve a complex optimization problem becomes a development trend. The application provides a reactive voltage optimization method of a power distribution network system comprising a small hydropower station.
Disclosure of Invention
The application provides a reactive voltage optimization method for a power distribution network system comprising a small hydropower station, and aims to solve the problems existing when a particle swarm algorithm and a genetic algorithm are singly adopted.
The technical scheme adopted by the application is as follows:
a method for reactive voltage optimization in a power distribution network system including small hydropower stations, the method comprising the steps of:
calculating the sensitivity of each node in a power distribution network system containing the small hydropower station;
comparing the sensitivity of each node, and selecting the node with the sensitivity higher than a sensitivity threshold value as a reactive compensation candidate configuration point;
establishing a power distribution network reactive power optimization model, wherein the power distribution network reactive power optimization model takes the minimum apparent power network loss of a power system as a target function;
solving the reactive power optimization model of the power distribution network by utilizing a particle swarm-genetic fusion algorithm to obtain the optimal reactive power compensation capacity, wherein the candidate configuration point corresponding to the optimal compensation capacity is a reactive power compensation point;
and adjusting the reactive power compensation quantity of the power distribution network system containing the small hydropower station through a reverse equivalent pairing principle, and performing network loss compensation on the power distribution network system.
Further, the sensitivity comprises a sensitivity S of a transmission power of the line to be dropped to an active powerPGIt can be expressed as:
Figure BDA0003154666630000021
wherein, PlossActive power loss; pGi、ViAnd thetaiInjecting active power, voltage amplitude and voltage phase angle of a node i in the power distribution network respectively;
the sensitivity S is measuredPGThe transformation is carried out as follows:
Figure BDA0003154666630000022
Figure BDA0003154666630000023
Figure BDA0003154666630000024
wherein G isijIs the conductivity between nodes i and j; b isijIs the susceptance between the nodes; thetaijIs the phase angle difference of the voltage between the nodes.
Further, the sensitivity also comprises the sensitivity S of node reactive power change to active network lossQGCan be represented as
Figure BDA0003154666630000025
Wherein QGi、ViAnd thetaiRespectively injecting reactive power, voltage amplitude and voltage phase of a node i in the power distribution networkAnd (4) an angle.
Further, the expression of the objective function is:
Figure BDA0003154666630000026
in the formula, SlossApparent power loss; z is a voltage out-of-bounds penalty factor; vi maxAnd Vi minIs the maximum and minimum allowed voltage in the node; Δ V is the voltage out-of-bounds value, expressed as:
Figure BDA0003154666630000027
in the formula, ViIs the voltage of node i, Vi maxAnd Vi minIs the maximum and minimum allowable voltage of the node.
Further, the method further comprises the step of setting constraint conditions of the power distribution network reactive power optimization model, wherein the constraint conditions comprise power equation constraints and inequality constraints, and the power balance equation is expressed as:
Figure BDA0003154666630000031
Figure BDA0003154666630000032
in the formula,. DELTA.Pi，ΔQiRespectively an active offset and a reactive offset of a node i; pGiActive power, Q, injected for node i in a distribution networkGiInjecting reactive power for a node i in the power distribution network; pDiExpressed as the active power of the load node; qDiIs the reactive power of the load node; qciIs a reactive compensation capacity; gijIs the conductivity between nodes i and j; b isijIs the susceptance between the nodes; thetaijIs the phase angle difference of the voltage between the nodes.
Further, the inequality constraints mainly include voltage constraints, transformer transformation ratios and reactive compensation capacity constraints, which are specifically expressed as:
Figure BDA0003154666630000033
in the formula, ViIs the voltage of node i, Vi max、Vi minThe upper and lower limits of the voltage of the node i; t isiFor transformer transformation ratio, Ti max、T iminThe upper limit and the lower limit of the transformation ratio of the on-load tap changing transformer are set; qcFor reactive compensation capacity, Qc max、Qi minThe upper and lower limits of the configuration capacity of the reactor are set.
Further, solving the power distribution network reactive power optimization model by using a particle swarm-genetic fusion algorithm to obtain the optimal compensation capacity, and the method comprises the following steps of:
step 501: inputting power distribution network line information, wherein the line information comprises node data, branch data and load data;
step 502: setting a learning factor c1、c2Maximum inertia weight coefficient ωmaxMinimum inertial weight coefficient ωminDetermining a proportionality coefficient k and a particle range; setting crossover operator p of genetic operationcMutation operator pm；
Step 503: encoding and generating initial population m and maximum velocity v of particlesmax；
Step 504: decoding and calculating the network load flow corresponding to each individual by adopting a Newton-Raphson method;
step 505: judging the iteration times T condition: if the iteration number T condition is met, executing step 510, and terminating the calculation, and if the iteration number T condition is not met, executing step 506;
step 506: calculating a fitness value F for each particle in the objective functioni(xi) Individual minimum value F (P)id) And a global minimum F (P)gd) If F isi(xi)＜F(pid) Then use the fitness value Fi(xi) Replace the individual minimum value F (P)id) (ii) a If Fi(xi)＜F(pgd) Then use the fitness value Fi(xi) Replacing the global minimum F (P)gd)；
Step 507: carrying out genetic operations of selection, crossing and variation on individuals entering a propagation library to generate a new population;
step 508: updating the speed and the position of the individual in the population, and specifically realizing the updating by the following formula:
v(i+1)d＝w·vid+c1r1(pid-xid)+c2r2(pgd-xid)
x(i+1)d＝xid+vid
in the formula, v(i+1)dRepresents the velocity at which the (i + 1) th particle "flies"; v. ofidThe velocity at which the ith particle "flies"; w is a velocity factor; p is a radical ofidThe optimal position searched for the ith particle so far, also called the individual extremum; p is a radical ofgdGlobal extrema searched thus far for the entire population; c. C1And c2Is a learning factor, also called acceleration constant, r1And r2Is [0,1 ]]Uniform random number, x, within a range(i+1)dDenotes the i +1 th particle position, xidRepresents the ith particle position;
step 509: coding, updating the population and returning to the step 504;
step 510: and outputting the optimal reactive compensation capacity after N iterations.
Further, the reactive power compensation quantity of the power distribution network system comprising the small hydropower stations is adjusted through a reverse equivalent pairing principle, and the method comprises the following steps:
sensitivity S according to injected reactive powerQGJudging the equivalent type of the generator set corresponding to each node comprises the following steps:
sensitivity S when node i injects reactive powerQGIf the sum is less than 0, the generator set corresponding to the node i is equivalent to an added-output generator set;
sensitivity S when node i injects reactive powerQGIf the node i is more than 0, the generator set corresponding to the node i is equivalent to a reduced output generator set;
sensitivity S when node i injects reactive powerQGWhen the node i is equal to 0, the generator set corresponding to the node i is equivalent to a balanced power generator set;
and regulating and controlling the reactive power compensation quantity of each node according to the equivalent type of the generator set.
The technical scheme of the application has the following beneficial effects:
the reactive power optimization of the power distribution network is carried out by utilizing the particle swarm-genetic fusion algorithm, so that the overall optimal solution can be approached more quickly, and the precision and the speed of convergence are improved; the minimum active network loss is calculated through a particle swarm-genetic fusion algorithm, the voltage distribution of the power distribution network is improved, and the loss of a power system can be effectively reduced.
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In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a flow chart of a reactive voltage optimization method of a power distribution network system including a small hydropower station according to an embodiment of the present application;
fig. 2 is a PSO-GA based reactive power optimization flow chart of the power distribution network provided in the embodiment of the present application;
fig. 3 is a network topology diagram of an IEEE33 node system.
Detailed Description
Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following examples do not represent all embodiments consistent with the present application. But merely as exemplifications of systems and methods consistent with certain aspects of the application, as recited in the claims.
Referring to fig. 1, a flow chart of a reactive voltage optimization method of a power distribution network system including a small hydropower station according to an embodiment of the present application is provided.
The application provides a reactive voltage optimization method of a power distribution network system comprising a small hydropower station, which comprises the following steps:
step 1: and calculating the active power loss and the reactive power loss of each node on the line by using a Newton-Raphson power flow algorithm.
The active power loss of each node on the line is calculated by using a Newton-Raphson algorithm, and the specific formula is as follows:
Figure BDA0003154666630000051
in the formula, PlossThe active power loss of the line (i, j) is shown, and i and j are nodes at two ends of the line (i, j) respectively; vi、VjVoltages of nodes i and j, respectively; n is the number of nodes of the whole distribution network, GijIs the conductivity between nodes i and j; b isijIs the susceptance between the nodes; thetaijIs the phase angle difference of the voltage between the nodes.
The reactive power loss of each node on the line is calculated by using a Newton-Raphson algorithm, and the specific formula is as follows:
Figure BDA0003154666630000052
in the formula, QlossThe reactive loss of the line (i, j) is shown, and i and j are nodes at two ends of the line (i, j) respectively; n is the node number of the whole distribution network, Vi、VjVoltages of nodes i and j, respectively; gijIs the conductivity between nodes i and j; b isijIs the susceptance between the nodes; thetaijIs the phase angle difference of the voltage between the nodes.
Calculating the reactive power loss of each node on the line, wherein the specific formula is as follows:
Figure BDA0003154666630000061
wherein, PlossFor active power loss on the line, QlossIs the reactive power loss on the line.
Step 2: and calculating the sensitivity of each node in the line, comparing the sensitivity of each node, and selecting the node with the sensitivity exceeding a sensitivity threshold value as a reactive compensation candidate configuration point.
The sensitivity S of transmission power of a network loss line to be reduced to active power is calculated firstlyPGIt can be expressed as:
Figure BDA0003154666630000062
wherein, PlossActive power loss; pGi、ViAnd thetaiInjecting active power, voltage amplitude and voltage phase angle of a node i in the power distribution network respectively;
the sensitivity S is measuredPGThe transformation is carried out as follows:
Figure BDA0003154666630000063
Figure BDA0003154666630000064
Figure BDA0003154666630000065
wherein G isijIs the conductivity between nodes i and j; b isijIs the susceptance between the nodes; thetaijIs the phase angle difference of the voltage between the nodes.
The sensitivity also includes a node reactive change to active network loss sensitivity SQG, which may be expressed as
Figure BDA0003154666630000066
Wherein QGi、ViAnd thetaiRespectively the injected reactive power, the voltage amplitude and the voltage phase angle of a node i in the power distribution network.
And comparing the sensitivity of each node, and selecting the node with the sensitivity exceeding a threshold value as a reactive compensation candidate configuration point. The threshold value is 10, namely, the node with the sensitivity value larger than 10 is selected as the reactive compensation candidate configuration point.
And step 3: establishing a power distribution network reactive power optimization model, wherein the power distribution network reactive power optimization model takes the minimum apparent power network loss of a power system as a target function; wherein the objective function is added as a penalty function when the node voltages of the candidate configuration points are outside a constraint range.
The design goal of the system is to minimize active power loss. The expression of the objective function is:
Figure BDA0003154666630000071
in the formula, SlossApparent power loss; z is a voltage out-of-bounds penalty factor; vi maxAnd Vi minIs the maximum and minimum allowed voltage in the node; Δ V is the voltage out-of-bounds value, expressed as:
Figure BDA0003154666630000072
in the formula, ViIs the voltage of node i, Vi maxAnd Vi minIs the maximum and minimum allowable voltage of the node.
And 4, step 4: and setting the constraint conditions of the reactive power optimization model of the power distribution network.
The constraint conditions include a power equation constraint and an inequality constraint, and the power balance equation is expressed as:
Figure BDA0003154666630000073
Figure BDA0003154666630000074
in the formula,. DELTA.Pi，ΔQiRespectively an active offset and a reactive offset of a node i; pGiActive power, Q, injected for node i in a distribution networkGiInjecting reactive power for a node i in the power distribution network; pDiExpressed as the active power of the load node; qDiIs the reactive power of the load node; qciIs a reactive compensation capacity; gijIs the conductivity between nodes i and j; b isijIs the susceptance between the nodes; thetaijIs the phase angle difference of the voltage between the nodes.
The inequality constraints mainly comprise voltage constraints, transformer transformation ratios and reactive compensation capacity constraints, and are specifically expressed as follows:
Figure BDA0003154666630000075
in the formula, ViIs the voltage of node i, Vi max、Vi minThe upper and lower limits of the voltage of the node i; t isiFor transformer transformation ratio, Ti max、Ti minThe upper limit and the lower limit of the transformation ratio of the on-load tap changing transformer are set; qcFor reactive compensation capacity, Qc max、Qi minThe upper and lower limits of the configuration capacity of the reactor are set.
TABLE 1 variable constraints
Figure BDA0003154666630000076
And 5: and solving the reactive power optimization model of the power distribution network by utilizing a particle swarm-genetic fusion algorithm to obtain the optimal reactive power compensation capacity, wherein the candidate configuration point corresponding to the optimal compensation capacity is a reactive power compensation point. Fig. 2 is a power distribution network reactive power optimization flow chart based on a PSO-GA algorithm provided in an embodiment of the present application. The method specifically comprises the following steps:
step 501: and inputting power distribution network line information, wherein the line information comprises node data, branch data and load data. An IEEE33 node system is used as a verification model. Fig. 3 is a network topology diagram of an IEEE33 node system. See table 2 for the line information for the IEEE33 node leg.
TABLE 2 IEEE33 node Branch parameters
Figure BDA0003154666630000081
Figure BDA0003154666630000091
Step 502: setting a learning factor c1、c2Maximum inertia weight coefficient ωmaxMinimum inertial weight coefficient ωminDetermining a proportionality coefficient k and a particle range; setting crossover operator p of genetic operationcMutation operator pm。
In the embodiment of the present application, the iteration number T is set to 50, and the learning factor c is set1＝1.5、c21.5, maximum inertial weight coefficient ωmax0.8, minimum inertial weight coefficient ωminDetermining a proportionality coefficient k to be 0.1-1, searching a space dimension D to be 3, coding individuals in a population in a binary mode, and setting the same coding digit for the two algorithms; setting crossover operator p of genetic operationc0.8, mutation operator pm＝0.01。
Step 503: encoding and generating initial population m and maximum velocity v of particlesmax。
In the embodiment of the present application, the maximum velocity v of the particles is setmax＝kxmaxWherein v ismaxIs the maximum value of the particle's "flight" velocity. Encoding and generating an initial population m-50;
step 504: decoding and calculating the network load flow corresponding to each individual by adopting a Newton-Raphson method.
The encoding is performed by the encoding formula X ═ a + d · δ.
Step 505: judging the iteration times T condition: if the iteration condition is satisfied, step 510 is performed, and if the iteration condition is not satisfied, step 506 is performed.
The iteration number T set in the embodiment of the present application is 50, and therefore it is determined whether the iteration number T is equal to 50: if T is 50, step 510 is performed, and if T is not equal to 50, step 504 is performed.
Step 506: calculating a fitness value F for each particle in the objective functioni(xi) Individual minimum value F (P)id) And a global minimum F (P)gd) If F isi(xi)＜F(pid) Then use the fitness value Fi(xi) Replace the individual minimum value F (P)id) (ii) a If Fi(xi)＜F(pgd) Then use the fitness value Fi(xi) Replacing the global minimum F (P)gd)；
Step 507: carrying out genetic operations of selection, crossing and variation on individuals entering a propagation library to generate a new population;
step 508: updating the speed and the position of the individual in the population, and specifically realizing the updating by the following formula:
v(i+1)d＝w·vid+c1r1(pid-xid)+c2r2(pgd-xid) (13)
x(i+1)d＝xid+vid (14)
in the formula, v(i+1)dRepresents the velocity at which the (i + 1) th particle "flies"; v. ofidThe velocity at which the ith particle "flies"; w is a velocity factor; p is a radical ofidThe optimal position searched for the ith particle so far, also called the individual extremum; p is a radical ofgdGlobal extrema searched thus far for the entire population; c. C1And c2Is a learning factor, also called acceleration constant, r1And r2Is [0,1 ]]Uniform random number, x, within a range(i+1)dDenotes the i +1 th particle position, xidRepresents the ith particle position;
step 509: coding, updating the population and returning to the step 504;
step 510: and outputting the optimal reactive compensation capacity after N iterations.
Under 50 repeated experiments, the particle swarm algorithm PSO, the genetic algorithm GA and the particle swarm-genetic fusion algorithm PSO-GA are respectively applied to a power distribution network reactive power optimization model to be solved, and the obtained objective function value is shown in table 3.
TABLE 3 objective function values calculated using different algorithms
Figure BDA0003154666630000101
As can be seen from the table, the variance of the particle swarm algorithm GA and the variance of the genetic algorithm PSO are both larger than that of the particle swarm-genetic fusion algorithm. Therefore, the particle swarm-genetic fusion algorithm provided by the application has more stable optimization results, and the search speed and the convergence accuracy of the algorithm are improved. The optimal reactive compensation capacity is the reactive compensation capacity corresponding to the minimum active power loss, so when min F is 0.024261kw, the optimal reactive compensation capacity is Qc＝0.08Mvar。
Step 6: the reactive power compensation amount of the power distribution network system comprising the small hydropower stations is adjusted through a reverse equivalent pairing principle, and the power distribution network system is subjected to network loss compensation, and the method specifically comprises the following steps:
sensitivity S according to the injected reactive powerQGJudging the equivalent type of the generator set corresponding to the node:
sensitivity S when node i injects reactive powerQGIf not more than 0, the generator set corresponding to the node i is equivalent to an added-output generator set GMINS；
Sensitivity S when node i injects reactive powerQGIf the node i is more than 0, the generator set corresponding to the node i is equivalent to a reduced output generator set GPLUS；
Sensitivity S when node i injects reactive powerQGAnd (5) equivalent the generator set corresponding to the node i to a balanced power generator set G (0)ZERO。
Then, the reactive power compensation amount of each node is regulated and controlled according to the equivalent types of different generator sets, and the network loss compensation of the power distribution network is realized, and the method comprises the following steps:
step 601: sensitivity according to reactive power SQGRespectively to the set G of the output-reducing generator setsPLUSEach of the derated and added derated generator sets GMINSSequencing each added output power generator set; the sensitivity element in the k-th output-reducing generator set after sorting is recorded as
Figure BDA0003154666630000102
And k is 1,2, …, n adds the number of the power generating sets; the sensitivity elements in the first sequenced added-out power generator set are recorded as
Figure BDA0003154666630000103
And l is 1,2, …, m adds the number of the power generating sets;
step 602: let k be 1, l be 1;
step 603: judging whether k is more than n, if so, determining a balanced power generator set GZEROTaking out an unused element as the sensitivity of the reactive power of the kth added output generator set
Figure BDA0003154666630000111
And this sensitivity is marked as used.
Step 604: judging whether l is greater than m, if so, collecting G from the balance power generator setZEROTaking out an unused element as the sensitivity of the reactive power of the first added output generator set
Figure BDA0003154666630000112
And this sensitivity is marked as used.
Step 605: reducing the reactive power of the kth output-reducing generating set by
Figure BDA0003154666630000113
Meanwhile, the reactive power of the first added power generator set is increased, and the reduction amount is
Figure BDA0003154666630000114
And is
Figure BDA0003154666630000115
Figure BDA0003154666630000116
The difference value of the kth active power of the output-reducing generating set and the lower limit of the active power of the generating set is calculated;
Figure BDA0003154666630000117
the difference value between the upper limit of the active power of the first added output generator set and the active power of the generator set is shown.
Step 606: judging whether the line to be subjected to loss reduction reaches the reactive power loss expected after loss reduction, or whether the line to be subjected to loss reduction reaches the lower regulation limit of the reduced output generator set, or whether the line to be subjected to loss reduction reaches the upper regulation limit of the added output generator set, and if so, executing a step 607; otherwise, let k be k +1 and l be l +1, return to step 603.
Lower regulation limit of the reduced output generator set
Figure BDA0003154666630000118
And n is the number of the output reducing generator sets.
The upper regulation limit of the added power generator set is
Figure BDA0003154666630000119
Wherein the content of the first and second substances,
Figure BDA00031546666300001110
and m is the number of the added output generator sets.
Step 607: and (6) ending.
The embodiments provided in the present application are only a few examples of the general concept of the present application, and do not limit the scope of the present application. Any other embodiments extended according to the scheme of the present application without inventive efforts will be within the scope of protection of the present application for a person skilled in the art.

Claims (8)

1. A method for reactive voltage optimization in a power distribution network system including small hydropower stations, the method comprising the steps of:
calculating the sensitivity of each node in a power distribution network system containing the small hydropower station;
comparing the sensitivity of each node, and selecting the node with the sensitivity higher than a sensitivity threshold value as a reactive compensation candidate configuration point;
establishing a power distribution network reactive power optimization model, wherein the power distribution network reactive power optimization model takes the minimum apparent power network loss of a power system as a target function;
solving the reactive power optimization model of the power distribution network by utilizing a particle swarm-genetic fusion algorithm to obtain the optimal compensation capacity, wherein the candidate configuration point corresponding to the optimal compensation capacity is a reactive power compensation point;
and adjusting the reactive power compensation quantity of the power distribution network system containing the small hydropower station through a reverse equivalent pairing principle, and performing network loss compensation on the power distribution network system.
2. Method for reactive voltage optimization in a power distribution grid system comprising small scale hydro-power plants, according to claim 1, characterized in that said sensitivity comprises the sensitivity S of the transmission power of the line to be dropped to the active powerPGIt can be expressed as:
Figure FDA0003154666620000011
wherein, PlossAs active powerLoss; pGi、ViAnd thetaiInjecting active power, voltage amplitude and voltage phase angle of a node i in the power distribution network respectively;
the sensitivity S is measuredPGThe transformation is carried out as follows:
Figure FDA0003154666620000012
Figure FDA0003154666620000013
Figure FDA0003154666620000014
wherein G isijIs the conductivity between nodes i and j; b isijIs the susceptance between the nodes; thetaijIs the phase angle difference of the voltage between the nodes.
3. Method for reactive voltage optimization of a power distribution grid system comprising small scale hydro-power plants according to claim 1 or 2, characterized in that said sensitivities further comprise the sensitivity of node reactive changes to active grid losses SQGCan be represented as
Figure FDA0003154666620000015
Wherein QGi、ViAnd thetaiRespectively the injected reactive power, the voltage amplitude and the voltage phase angle of a node i in the power distribution network.
4. The method of reactive voltage optimization for a small hydropower station-containing power distribution grid system of claim 1, wherein the objective function is expressed as:
Figure FDA0003154666620000021
in the formula, SlossApparent power loss; z is a voltage out-of-bounds penalty factor; vimaxAnd ViminIs the maximum and minimum allowed voltage in the node; Δ V is the voltage out-of-bounds value, expressed as:
Figure FDA0003154666620000022
in the formula, ViIs the voltage of node i, VimaxAnd ViminIs the maximum and minimum allowable voltage of the node.
5. The method of reactive voltage optimization for an electrical power distribution grid system including small scale hydro-power plants as claimed in claim 1 further comprising setting constraints of the reactive power optimization model for the electrical power distribution grid, the constraints including power equation constraints and inequality constraints, the power balance equation expressed as:
Figure FDA0003154666620000023
Figure FDA0003154666620000024
in the formula,. DELTA.Pi，ΔQiRespectively an active offset and a reactive offset of a node i; pGiActive power, Q, injected for node i in a distribution networkGiInjecting reactive power for a node i in the power distribution network; pDiExpressed as the active power of the load node; qDiIs the reactive power of the load node; qciIs a reactive compensation capacity; gijIs the conductivity between nodes i and j; b isijIs the susceptance between the nodes; thetaijIs the phase angle difference of the voltage between the nodes.
6. The reactive voltage optimization method for a small hydropower station-containing power distribution grid system of claim 5, wherein the inequality constraints mainly include a voltage constraint, a transformer transformation ratio and a reactive compensation capacity constraint, and are specifically expressed as:
Figure FDA0003154666620000025
in the formula, ViIs the voltage of node i, Vimax、ViminThe upper and lower limits of the voltage of the node i; t isiFor transformer transformation ratio, Timax、TiminThe upper limit and the lower limit of the transformation ratio of the on-load tap changing transformer are set; qcFor reactive compensation capacity, Qcmax、QiminThe upper and lower limits of the configuration capacity of the reactor are set.
7. The reactive voltage optimization method of the power distribution network system comprising the small hydropower stations, as set forth in claim 1, wherein the power distribution network reactive power optimization model is solved by a particle swarm-genetic fusion algorithm to obtain an optimal compensation capacity, and the method comprises the following steps:
step 501: inputting power distribution network line information, wherein the line information comprises node data, branch data and load data;
step 502: setting a learning factor c1、c2Maximum inertia weight coefficient ωmaxMinimum inertial weight coefficient ωminDetermining a proportionality coefficient k and a particle range; setting crossover operator p of genetic operationcMutation operator pm；
Step 503: encoding and generating initial population m and maximum velocity v of particlesmax；
Step 504: decoding and calculating the network load flow corresponding to each individual by adopting a Newton-Raphson method;
step 505: judging the iteration times T condition: if the iteration number T condition is met, executing step 510, and terminating the calculation, and if the iteration number T condition is not met, executing step 506;
step 506: calculating a fitness value F for each particle in the objective functioni(xi) Individual minimum value F (P)id) And a global minimum F (P)gd) If F isi(xi)＜F(pid) Then use the fitness value Fi(xi) Replace the individual minimum value F (P)id) (ii) a If Fi(xi)＜F(pgd) Then use the fitness value Fi(xi) Replacing the global minimum F (P)gd)；
Step 507: carrying out genetic operations of selection, crossing and variation on individuals entering a propagation library to generate a new population;
step 508: updating the speed and the position of the individual in the population, and specifically realizing the updating by the following formula:
v(i+1)d＝w·vid+c1r1(pid-xid)+c2r2(pgd-xid)
x(i+1)d＝xid+vid
in the formula, v(i+1)dRepresents the velocity at which the (i + 1) th particle "flies"; v. ofidThe velocity at which the ith particle "flies"; w is a velocity factor; p is a radical ofidThe optimal position searched for the ith particle so far, also called the individual extremum; p is a radical ofgdGlobal extrema searched thus far for the entire population; c. C1And c2Is a learning factor, also called acceleration constant, r1And r2Is [0,1 ]]Uniform random number, x, within a range(i+1)dDenotes the i +1 th particle position, xidRepresents the ith particle position;
step 509: coding, updating the population and returning to the step 504;
step 510: and outputting the optimal reactive compensation capacity after N iterations.
8. The reactive voltage optimization method for a power distribution grid system including small scale hydro-power plants as claimed in claim 3, wherein said adjusting the reactive power compensation amount of the power distribution grid system including small scale hydro-power plants by inverse equivalence pairing rules comprises:
sensitivity S according to injected reactive powerQGJudging the equivalent type of the generator set corresponding to each node comprises the following steps:
sensitivity S when node i injects reactive powerQGIf the sum is less than 0, the generator set corresponding to the node i is equivalent to an added-output generator set;
sensitivity S when node i injects reactive powerQGIf the node i is more than 0, the generator set corresponding to the node i is equivalent to a reduced output generator set;
sensitivity S when node i injects reactive powerQGWhen the node i is equal to 0, the generator set corresponding to the node i is equivalent to a balanced power generator set;
and regulating and controlling the reactive power compensation quantity of each node according to the equivalent type of the generator set.
CN202110781640.0A 2021-07-08 2021-07-08 Reactive voltage optimization method for power distribution network system comprising small hydropower station Pending CN113517723A (en)

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