CN112310958A - Power grid dispatching optimization method considering power grid load rate and time sequence load change - Google Patents

Power grid dispatching optimization method considering power grid load rate and time sequence load change Download PDF

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CN112310958A
CN112310958A CN202011015012.3A CN202011015012A CN112310958A CN 112310958 A CN112310958 A CN 112310958A CN 202011015012 A CN202011015012 A CN 202011015012A CN 112310958 A CN112310958 A CN 112310958A
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power grid
monkey
discrete
switch
distribution network
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CN112310958B (en
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张�杰
马世乾
王天昊
李振斌
崇志强
于光耀
陈培育
韩磊
袁中琛
李国栋
刘亚丽
刘云
王峥
胡晓辉
李树青
李树鹏
吴磊
丁一
戚艳
孙冰
李云飞
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Electric Power Research Institute of State Grid Tianjin Electric Power Co Ltd
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State Grid Corp of China SGCC
State Grid Tianjin Electric Power Co Ltd
Electric Power Research Institute of State Grid Tianjin Electric Power 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
    • 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]

Abstract

The invention relates to a power grid dispatching optimization method considering power grid load rate and time sequence load change, which comprises the following steps: step 1, initializing variables based on input power distribution network parameters, and setting discrete monkey group algorithm initial parameters: step 2, adopting a discrete monkey group algorithm to perform crawling process operation; step 3, carrying out hope-jump process operation by adopting a discrete monkey group algorithm; step 4, adopting a discrete monkey group algorithm to perform process operation; step 5, termination judgment is carried out, if the termination criterion is met, the algorithm is ended to output an optimal result; if not, steps 2-4 are repeated until the termination criteria are met. According to the method, under the constraint condition of two dimensions of a section switch and a tie switch in a power distribution network, the network loss of the power distribution network is taken as a research object, and the optimal combination of the section switch and the tie switch in the power distribution network is obtained by optimizing the network loss in a period of time instead of a certain time section in the power distribution network.

Description

Power grid dispatching optimization method considering power grid load rate and time sequence load change
Technical Field
The invention belongs to the technical field of load rate balance control of a substation cluster in a power distribution system, relates to a power grid dispatching optimization method, and particularly relates to a power grid dispatching optimization method considering power grid load rate and time sequence load change.
Background
When a substation in a power grid is planned and designed, the requirement on the load rate of each substation is generally ignored according to the load prediction result of a planned area and the existing substation constitution situation, so that the load is unevenly distributed on each substation, and in addition, the space-time distribution characteristics of the load are considered, even if different areas at the same time are different in load type, different load characteristics are presented, so that the phenomenon of heavy load or light load of the substation in the power grid can occur. In an actual power distribution system, in order to ensure the reliability of power supply, substation feeders with short electrical distance are often connected through a tie line, so that substation loads can be reasonably and uniformly arranged by changing the states of tie switches in a power distribution network and section switches on the feeders, namely network reconstruction, and the power grid loss is reduced.
The distribution network has more interconnection switches and section switches, and the calculation difficulty of carrying out operation optimization on the switching equipment of the distribution network on a time section is very high; furthermore, in order to ensure the reliability of power supply and reduce the complexity of operation management, the on-off state of the switch device is not allowed to be frequently changed, and a dispatcher needs to reasonably adjust the states of a contact switch and a section switch on a feeder line in a power distribution network in time according to the change of time sequence load and the load rate of the power grid, so that the difficulty in determining the operation decision of a large number of switch devices in the time dimension is higher.
In summary, it is necessary to optimize the power grid loss in a certain period of time rather than a certain time section to obtain the optimal switch state combination in the power grid, so that a power grid scheduling optimization method considering the power grid load rate and the time sequence load change is urgently needed for a scheduling department to assist scheduling personnel in making scientific and reasonable decisions on the states of the section switches and the interconnection switches in the power grid, thereby reducing the power grid loss and more reasonably utilizing resources.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a power grid dispatching optimization method considering the load rate and the time sequence load change of a power grid.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a power grid dispatching optimization method considering power grid load rate and time sequence load change is characterized by comprising the following steps: the method comprises the following steps:
step 1, initializing variables based on input power distribution network parameters, and setting discrete monkey group algorithm initial parameters;
step 2, adopting a discrete monkey group algorithm to perform crawling process operation;
step 3, carrying out hope-jump process operation by adopting a discrete monkey group algorithm;
step 4, adopting a discrete monkey group algorithm to perform process operation;
step 5, termination judgment is carried out, if the termination criterion is met, the algorithm is ended to output an optimal result; if not, steps 2-4 are repeated until the termination criteria are met.
Further: the network parameters of the power distribution network input in the step one comprise the number of feeder lines in the power distribution network, the number of normally closed switches of each feeder line and the number of interconnection switches.
Further: initializing variables in the first step specifically comprises the following steps:
establishing a model objective function, see expression (1):
Min G(x) (1)
wherein G (x) is network loss in the power distribution network, and the variable x is a switching variable combination in the power distribution network;
the constraints of the established model comprise constraints of two dimensions of space and time:
(1) time dimension constraint
Including switching frequency constraints, which means that the number of operations of the sectionalizing switch and the tie switch in the optimization period has a certain limit,
see expression (2):
Figure BDA0002698768950000021
in the formula
Figure BDA0002698768950000022
Is an exclusive or mathematical operator; nx1 and nx2 are the maximum action times of the head-end circuit breaker or the sectional switch and the tie switch in the T time period;
(2) spatial dimension constraints comprising:
a. tidal current equation constraint, see expression (3)
Figure BDA0002698768950000023
The power supply system comprises a power supply, a power supply controller and a power supply controller, wherein Px, t, Qx and t are respectively active power and reactive power injected at the x-th feeder load at the moment t; ui, t represents the voltage amplitude loaded by the x-th feeder line at the t moment; gxj, Bxj and xj respectively form conductance, susceptance and phase angle difference on a connecting line between the x-th feeder line and the j-th feeder line at the t-th moment;
b. the inequality constraints of voltage and transformer capacity are shown in expression (4);
Umin≤Ux,t≤Umax
|Lx,j|≤Lmax
Si≤Simax (4)
the Ux, t, Si and Lxj respectively represent the voltage amplitude of the x-th feeder line load at the time t, the actual load of the ith transformer and the transmission capacity of a connecting line between the x-th line and the j-th line;
c. a radiation operation constraint;
when the distribution network actually runs, the topological structure of the distribution network needs to be ensured to be radial, so that three switches, namely a feeder line head end breaker, a tie switch on the feeder line and a feeder line head end breaker connected with the feeder line, need to be ensured that only two switches are in an open state, see expression (5):
onoff1(x,t)+onoff2(x,t)+onoff1(j,t)-2＝0 (5)。
further: in the step 1, initial parameters of a discrete monkey group algorithm are set, and the method specifically comprises the following steps:
firstly, setting a tie switch on each feeder line to be in an open state, setting a breaker at the head end of the feeder line to be in a closed state, and then performing variation on a certain switch at a certain time by using a random number method to serve as an initial solution; the invention utilizes an improved discrete monkey group algorithm to solve, the scale of the monkey group is set as M, and for the ith monkey in the monkey group, the position of the ith monkey can be defined as:
Xi＝(xi,1,xi,2,…,xi,n)Txi,n∈{0,1}
wherein, XiCorresponds to a switch in the power distribution system, the value x on each componenti,1,xi,2,…,xi,nIndicating the state on the corresponding switch, 0 for on, 1 for off, so that the current position of each monkey is the set of switch combinations for the optimized model.
Further: step 2, performing crawling process operation by adopting a discrete monkey group algorithm, and specifically comprising the following steps:
step 2.1 random generation of vectors
Figure BDA0002698768950000031
See expression (6)
Figure BDA0002698768950000032
Wherein, Δ xi,jIs a randomly generated number from the set 0,1, j ∈ {1,2, …, n };
step 2.2. order
Figure BDA0002698768950000033
Computing
Figure BDA0002698768950000034
Step 2.3 if:
Figure BDA0002698768950000035
then order
Figure BDA0002698768950000036
Otherwise, if:
Figure BDA0002698768950000037
then order
Figure BDA0002698768950000038
Step 2.4 repeating steps 2.1 to 2.3 until the objective function value is not changed for a plurality of continuous iterations or reaches a preset iteration number NC,L。
Further: step 3, performing the operation of the hope-jump process by adopting a discrete monkey group algorithm, and specifically comprising the following steps:
for the ith monkey, the procedure was as follows:
step 3.1 random Generation of column vectors Bi(j) And obtaining a vector: b isi＝[Bi(1),Bi(2),...,Bi(n)]I.e. a group of switch combinations in a period of time, but because the model is coupled in a time dimension, the combination can be avoidedProducing a large number of invalid solutions, first on the generated BiMaking a decision in the time dimension at [0, N]If the number of switching operations has reached an upper limit within a time period, the switching state remains unchanged since the last change, and secondly, for B generatediJudging in the spatial dimension so that the solution satisfies all constraint conditions and is a set of feasible solutions;
step 3.2 if G (B)i)<G(Xi) Then let Xi＝BiTurning to the climbing process, otherwise, turning to the next step 3.3;
step 3.3 repeat steps 3.1 to 3.2 until the objective function value is not changed for a plurality of continuous iterations or reaches a preset iteration number NC,L
Further: step 4, a discrete monkey group algorithm is adopted to turn over process operation, and the method specifically comprises the following steps:
step 4.1, the iteration number k is k +1, and a real number d is randomly generated at [0,1 ];
step 4.2 calculate the mean pj of the monkey population:
Figure BDA0002698768950000041
step 4.3 pairs
Figure BDA0002698768950000042
Calculating a ═ d | pj-Xi(j) If a is more than or equal to 1, Xi'(j)＝max(Xi(j) +1,1), otherwise Xi’(j)＝max(Xi(j)+0,1)；
Step 4.4 likewise to avoid generating a large number of invalid solutions, the X generated is subjected toi' (j) judging in both space-time dimensions if X is aboveiIf' (j) is feasible, let Xi(j)＝Xi' (j), ending the scrolling process, and turning to the climbing process, otherwise, repeating the steps 4.1 to 4.4 until a feasible solution is found.
Further: step 5 termination interpretation includes two termination criteria:
(1) the iteration number k reaches the maximum value;
(2) the optimal solution obtained does not change for successive generations.
The invention has the advantages and positive effects that:
under the constraint conditions of two dimensions of a section switch and a tie switch in a power distribution network, the optimal combination of the section switch and the tie switch in the power distribution network is obtained by taking the network loss of the power distribution network as a research object and optimizing the network loss in a period of time instead of a certain time section in the power distribution network. Because the number of decision variables in the optimized model is increased by geometric multiples compared with the conventional network reconstruction, the optimized model is solved based on the improved discrete monkey group algorithm, and the optimal combination of the switches in the power distribution network is obtained by utilizing the better optimizing capability and the convergence speed of the monkey group algorithm; secondly, different types of loads in different areas are considered, and different types of actual load curves introduced into the feeder line of the transformer substation in the power distribution network are simulated; finally, in the improved monkey swarm algorithm, the particularity of the model is combined, and the randomly generated variables are limited by adopting a manual strategy to form a group of feasible solutions, so that a large number of invalid solutions are avoided, the calculation time is saved, and the optimization speed is increased.
Drawings
FIG. 1 is a flow chart of a power grid dispatching optimization method taking into account power grid load rate and time sequence load changes according to the invention;
fig. 2 is a power distribution network topology structure diagram according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the following embodiments, which are illustrative, not restrictive, and the scope of the invention is not limited thereto.
A power grid dispatching optimization method considering power grid load rate and time sequence load change, please refer to fig. 1, which is characterized in that: the method comprises the following steps:
step 1, initializing variables based on input power distribution network parameters, and setting discrete monkey group algorithm initial parameters;
the input power distribution network parameters comprise the number of feeders in the distribution network, the number of normally closed switches of each feeder and the number of interconnection switches.
Initializing variables specifically includes:
establishing a model objective function, see expression (1):
Min G(x) (1)
wherein G (x) is network loss in the power distribution network, and the variable x is a switching variable combination in the power distribution network;
the constraints of the established model comprise constraints of two dimensions of space and time:
(1) time dimension constraint
Including switching frequency constraints, which means that the number of operations of the sectionalizing switch and the tie switch in the optimization period has a certain limit,
see expression (2):
Figure BDA0002698768950000051
in the formula
Figure BDA0002698768950000052
Is an exclusive or mathematical operator; nx1, nx2 are maximum action times of the head-end breaker or the section switch and the tie switch in the T period.
(2) Spatial dimension constraints comprising:
b. tidal current equation constraint, see expression (3)
Figure BDA0002698768950000053
The power supply system comprises a power supply, a power supply controller and a power supply controller, wherein Px, t, Qx and t are respectively active power and reactive power injected at the x-th feeder load at the moment t; ui, t represents the voltage amplitude loaded by the x-th feeder line at the t moment; gxj, Bxj and xj respectively form conductance, susceptance and phase angle difference on a connecting line between the x-th feeder line and the j-th feeder line at the t-th moment;
b. the inequality constraints of voltage and transformer capacity are shown in expression (4);
Umin≤Ux,t≤Umax
|Lx,j|≤Lmax
Si≤Simax (4)
the Ux, t, Si and Lxj respectively represent the voltage amplitude of the x-th feeder line load at the time t, the actual load of the ith transformer and the transmission capacity of a connecting line between the x-th line and the j-th line;
d. a radiation operation constraint;
when the distribution network actually runs, the topological structure of the distribution network needs to be ensured to be radial, so that three switches, namely a feeder line head end breaker, a tie switch on the feeder line and a feeder line head end breaker connected with the feeder line, need to be ensured that only two switches are in an open state, see expression (5):
onoff1(x,t)+onoff2(x,t)+onoff1(j,t)-2＝0 (5)。
setting initial parameters of a discrete monkey group algorithm, specifically:
because the optimized model is complex and has a coupling relation in a time dimension, the randomly generated variables hardly satisfy the constraint condition as an initial solution. Firstly, setting a tie switch on each feeder line to be in an open state, setting a breaker at the head end of the feeder line to be in a closed state, and then performing variation on a certain switch at a certain time by using a random number method to serve as an initial solution; the invention utilizes an improved discrete monkey group algorithm to solve, the scale of the monkey group is set as M, and for the ith monkey in the monkey group, the position of the ith monkey can be defined as:
Xi＝(xi,1,xi,2,…,xi,n)Txi,n∈{0,1}
wherein, XiCorresponds to a switch in the power distribution system, the value x on each componenti,1,xi,2,…,xi,nIndicating the state on the corresponding switch, 0 for on, 1 for off, so that the current position of each monkey is the set of switch combinations for the optimized model.
Step 2, performing crawling process operation by adopting a discrete monkey group algorithm, and specifically comprising the following steps:
step 2.1 random generation of vectors
Figure BDA0002698768950000061
See expression (6)
Figure BDA0002698768950000062
Wherein, Δ xi,jIs a randomly generated number from the set 0,1, j ∈ {1,2, …, n };
step 2.2. order
Figure BDA0002698768950000063
Computing
Figure BDA0002698768950000064
Step 2.3 if:
Figure BDA0002698768950000071
then order
Figure BDA0002698768950000072
Otherwise, if:
Figure BDA0002698768950000073
then order
Figure BDA0002698768950000074
Step 2.4 repeating steps 2.1 to 2.3 until the objective function value is not changed for a plurality of continuous iterations or reaches a preset iteration number NC,L。
Step 3, performing the operation of the hope-jump process by adopting a discrete monkey group algorithm, which specifically comprises the following steps:
for the ith monkey, the procedure was as follows:
step 3.1 random Generation of column vectors Bi(j) And obtaining a vector: b isi＝[Bi(1),Bi(2),...,Bi(n)]I.e. a group of switch combinations in a period of time, but because the model is coupled in a time dimension, in order to avoid generating a large number of invalid solutions, the generated B is firstly combinediMaking a decision in the time dimension at [0, N]If the number of switching operations has reached an upper limit within a time period, the switching state remains unchanged since the last change, and secondly, for B generatediJudging in the spatial dimension so that the solution satisfies all constraint conditions and is a set of feasible solutions;
step 3.2 if G (B)i)<G(Xi) Then let Xi＝BiTurning to a climbing process;
step 3.3 repeat steps 3.1 to 3.2 until the objective function value is not changed for a plurality of continuous iterations or reaches a preset iteration number NC,L。
Step 4, adopting a discrete monkey group algorithm to turn over process operation, and specifically comprising the following steps:
step 4.1, the iteration number k is k +1, and a real number d is randomly generated at [0,1 ];
step 4.2 calculate the mean pj of the monkey population:
Figure BDA0002698768950000075
step 4.3 pairs
Figure BDA0002698768950000076
Calculating a ═ d | pj-Xi(j) If a is more than or equal to 1, Xi'(j)＝max(Xi(j) +1,1), otherwise Xi’(j)＝max(Xi(j)+0,1)；
Step 4.4 likewise to avoid generating a large number of invalid solutions, the X generated is subjected toi' (j) judging in both space-time dimensions if X is abovei’(j)If possible, then let Xi(j)＝Xi' (j), ending the scrolling process, and turning to the climbing process, otherwise, repeating the steps 4.1 to 4.4 until a feasible solution is found.
And step 5, termination judgment:
the whole algorithm has the following two termination criteria:
(1) the number of iterations k reaches a maximum value.
(2) The optimal solution obtained does not change for successive generations.
If the termination criterion is met, the algorithm is ended, the optimal result is output, and if the termination criterion is not met, the steps 2 to 4 are repeated until the termination criterion is met.
The effectiveness of the invention is demonstrated by the following specific examples. Suppose that four substations exist in a certain distribution network, each substation contains two 35/10kV main transformers, and the topological structure is shown in fig. 2. R can be obtained by calculation according to main transformer parametersi＝0.219Ω,Xi3.111 Ω, the feed line impedance and inductive reactance are Rj＝0.219Ω/km,XjThe load on each main transformer feeder is simulated by using IEEE RTS load data, the load types on different feeders are different, including industry, agriculture, commerce, municipal administration and random combinations thereof, and the simulation results are shown in table 1.
TABLE 1 simulation results using the improved discrete monkey swarm algorithm
Figure BDA0002698768950000081
Taking the second monkey for example after 20 iterations, the initial variation position of the monkey is the first outgoing line of the first main transformer of the fourth substation for transferring the load to the fourth outgoing line of the second main transformer of the second substation at the twelfth moment, the final switch-off states of the first main transformer of the first substation and the breaker at the head end of the first transformer feeder line of the second main transformer connected with the first main transformer feeder line are respectively shown in table 2, (feeder lines 1,2, 3 and 4 are the four outgoing lines of the first main transformer of the first substation, and feeder lines 1,2 and 3 are respectively connected with the first transformer feeder lines of the second main transformer, namely 9, 10 and 11), it is clear that the load on the feeder is transferred through the tie line, the feeder 1 at a first moment in time loads the load through the tie switch to the feeder 9, and the feeder 10 at a third to twenty-fourth moment in time loads are transferred to the feeder 2.
Table 2 optimal solution state of partial feeder head end circuit breaker
Figure BDA0002698768950000091
Although the embodiments and figures of the present invention have been disclosed for illustrative purposes, those skilled in the art will appreciate that: various substitutions, changes and modifications are possible without departing from the spirit and scope of the invention and the appended claims, and therefore the scope of the invention is not limited to the disclosure of the embodiments and figures.

Claims (8)

1. A power grid dispatching optimization method considering power grid load rate and time sequence load change is characterized by comprising the following steps: the method comprises the following steps:
step 1, initializing variables based on input power distribution network parameters, and setting discrete monkey group algorithm initial parameters;
step 2, adopting a discrete monkey group algorithm to perform crawling process operation;
step 3, carrying out hope-jump process operation by adopting a discrete monkey group algorithm;
step 4, adopting a discrete monkey group algorithm to perform process operation;
step 5, termination judgment is carried out, if the termination criterion is met, the algorithm is ended to output an optimal result; if not, steps 2-4 are repeated until the termination criteria are met.
2. The power grid dispatching optimization method considering power grid load rate and time sequence load change according to claim 1, characterized in that: the network parameters of the power distribution network input in the step one comprise the number of feeder lines in the power distribution network, the number of normally closed switches of each feeder line and the number of interconnection switches.
3. The power grid dispatching optimization method considering power grid load rate and time sequence load change according to claim 2, wherein initializing variables in step 1 specifically comprises:
establishing a model objective function, see expression (1):
Min G(x) (1)
wherein G (x) is network loss in the power distribution network, and the variable x is a switching variable combination in the power distribution network;
the constraints of the established model comprise constraints of two dimensions of space and time:
(1) time dimension constraint
Including switching frequency constraints, which means that the number of operations of the sectionalizing switch and the tie switch in the optimization period has a certain limit,
see expression (2):
Figure FDA0002698768940000011
Figure FDA0002698768940000012
^ in the formula is an exclusive or mathematical operator; nx1 and nx2 are the maximum action times of the head-end circuit breaker or the sectional switch and the tie switch in the T time period;
(2) spatial dimension constraints comprising:
a. tidal current equation constraint, see expression (3)
Figure FDA0002698768940000021
The power supply system comprises a power supply, a power supply controller and a power supply controller, wherein Px, t, Qx and t are respectively active power and reactive power injected at the x-th feeder load at the moment t; ui, t represents the voltage amplitude loaded by the x-th feeder line at the t moment; gxj, Bxj and xj respectively form conductance, susceptance and phase angle difference on a connecting line between the x-th feeder line and the j-th feeder line at the t-th moment;
b. the inequality constraints of voltage and transformer capacity are shown in expression (4);
Umin≤Ux,t≤Umax
|Lx,j|≤Lmax
Si≤Simax (4)
the Ux, t, Si and Lxj respectively represent the voltage amplitude of the x-th feeder line load at the time t, the actual load of the ith transformer and the transmission capacity of a connecting line between the x-th line and the j-th line;
c. a radiation operation constraint;
when the distribution network actually runs, the topological structure of the distribution network needs to be ensured to be radial, so that three switches, namely a feeder line head end breaker, a tie switch on the feeder line and a feeder line head end breaker connected with the feeder line, need to be ensured that only two switches are in an open state, see expression (5):
onoff1(x,t)+onoff2(x,t)+onoff1(j,t)-2＝0 (5)。
4. the power grid dispatching optimization method considering power grid load rate and time sequence load change according to claim 3, wherein the discrete monkey group algorithm initial parameters are set in step 1, and specifically are as follows:
firstly, setting a tie switch on each feeder line to be in an open state, setting a breaker at the head end of the feeder line to be in a closed state, and then performing variation on a certain switch at a certain time by using a random number method to serve as an initial solution; the invention utilizes an improved discrete monkey group algorithm to solve, the scale of the monkey group is set as M, and for the ith monkey in the monkey group, the position of the ith monkey can be defined as:
Xi＝(xi,1,xi,2,…,xi,n)Txi,n∈{0,1}
wherein, XiCorresponds to a switch in the power distribution system, the value x on each componenti,1,xi,2,…,xi,nIndicating the state on the corresponding switch, 0 for on, 1 for off, so that the current position of each monkey is the set of switch combinations for the optimized model.
5. The power grid dispatching optimization method considering power grid load rate and time sequence load change according to claim 1, wherein step 2 adopts a discrete monkey swarm algorithm to perform climbing process operation, and specifically comprises the following steps:
step 2.1 random generation of vectors
Figure FDA0002698768940000022
See expression (6)
Figure FDA0002698768940000023
Wherein, Δ xi,jIs a randomly generated number from the set 0,1, j ∈ {1,2, …, n };
step 2.2. order
Figure FDA0002698768940000031
Computing
Figure FDA0002698768940000032
Step 2.3 if:
Figure FDA0002698768940000033
then order
Figure FDA0002698768940000034
Otherwise, if:
Figure FDA0002698768940000035
then order
Figure FDA0002698768940000036
Step 2.4 repeating steps 2.1 to 2.3 until the objective function value is not changed for a plurality of continuous iterations or reaches a preset iteration number NC,L。
6. The power grid dispatching optimization method considering power grid load rate and time sequence load change according to claim 5, wherein the step 3 adopts a discrete monkey group algorithm to perform hope-jump process operation, and specifically comprises the following steps:
for the ith monkey, the procedure was as follows:
step 3.1 random Generation of column vectors Bi(j) And obtaining a vector: b isi＝[Bi(1),Bi(2),...,Bi(n)]I.e. a group of switch combinations in a period of time, but because the model is coupled in a time dimension, in order to avoid generating a large number of invalid solutions, the generated B is firstly combinediMaking a decision in the time dimension at [0, N]If the number of switching operations has reached an upper limit within a time period, the switching state remains unchanged since the last change, and secondly, for B generatediJudging in the spatial dimension so that the solution satisfies all constraint conditions and is a set of feasible solutions;
step 3.2 if G (B)i)<G(Xi) Then let Xi＝BiTurning to the climbing process, otherwise, turning to the next step 3.3;
step 3.3 repeat steps 3.1 to 3.2 until the objective function value is not changed for a plurality of continuous iterations or reaches a preset iteration number NC,L。
7. The power grid dispatching optimization method considering power grid load rate and time sequence load change according to claim 6, wherein step 4 adopts a discrete monkey group algorithm to turn over process operation, and specifically comprises the following steps:
step 4.1, the iteration number k is k +1, and a real number d is randomly generated at [0,1 ];
step 4.2 calculate the mean pj of the monkey population:
Figure FDA0002698768940000037
step 4.3 pairs
Figure FDA0002698768940000038
Calculating a ═ d | pj-Xi(j) If a is more than or equal to 1, Xi'(j)＝max(Xi(j) +1,1), otherwise Xi'(j)＝max(Xi(j)+0,1)；
Step 4.4 likewise to avoid generating a large number of invalid solutions, the X generated is subjected toi' (j) judging in both space-time dimensions if X is aboveiIf' (j) is feasible, let Xi(j)＝Xi' (j), ending the scrolling process, and turning to the climbing process, otherwise, repeating the steps 4.1 to 4.4 until a feasible solution is found.
8. The method of claim 7, wherein the step 5 termination interpretation comprises two termination criteria:
(1) the iteration number k reaches the maximum value;
(2) the optimal solution obtained does not change for successive generations.
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