CN111342461B - Power distribution network optimal scheduling method and system considering dynamic reconfiguration of network frame - Google Patents

Power distribution network optimal scheduling method and system considering dynamic reconfiguration of network frame Download PDF

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CN111342461B
CN111342461B CN202010236133.4A CN202010236133A CN111342461B CN 111342461 B CN111342461 B CN 111342461B CN 202010236133 A CN202010236133 A CN 202010236133A CN 111342461 B CN111342461 B CN 111342461B
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CN111342461A (en
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倪识远
张林垚
施鹏佳
林毅
吴桂联
郑洁云
林婷婷
陈浩
姜世公
王云飞
刘艳茹
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State Grid Economic And Technological Research Institute Co LtdB412 State Grid Office
State Grid Fujian Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Fujian Electric Power Co Ltd
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State Grid Economic And Technological Research Institute Co LtdB412 State Grid Office
State Grid Fujian Electric Power Co Ltd
Economic and Technological Research Institute of State Grid Fujian 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
    • 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
    • H02J3/008—Circuit arrangements for ac mains or ac distribution networks involving trading of energy or energy transmission rights
    • 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
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers

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Abstract

The invention relates to a power distribution network optimal scheduling method and system considering dynamic reconfiguration of a network frame, wherein an active power distribution network element is firstly modeled in a time sequence mode; and then, with the economy of power distribution network scheduling and the stability of the rapid voltage as targets, establishing an active double-layer optimization scheduling model considering the dynamic reconstruction of the network, carrying out decimal coding on the branch in each basic loop, setting an iteration condition of a network frame population, adding a radiation type constraint condition of the power distribution network frame into an iteration strategy, and solving the active double-layer optimization scheduling model. The invention can effectively reduce the operation cost of the power distribution network, smooth the voltage level of the power distribution system and improve the voltage stability of the system.

Description

Power distribution network optimal scheduling method and system considering dynamic reconfiguration of network frame
Technical Field
The invention relates to the technical field of power distribution network safety and scheduling, in particular to a power distribution network optimal scheduling method and system considering dynamic reconfiguration of a network frame.
Background
An Active Distribution Network (ADN) has a flexible network structure, and can deeply excavate the potential of a Distribution network through economic scheduling modes such as a Distributed Generation (DG) and Demand Side Management (DSM), thereby meeting the load growth Demand and the distributed power access Demand. Therefore, the network reconstruction technology and the active management means are important ways for realizing the optimal scheduling of the active power distribution network. The network reconstruction technology changes the topological structure of the network by changing the operation state of a switch in the power distribution network, and the active management means can actively adjust the power flow of the power distribution network, and the two supplement each other, so that the network loss of the power distribution network can be greatly reduced, and the economical efficiency of the operation of the power distribution network is improved. However, dynamic reconfiguration and active management techniques also present significant challenges to the safe and stable operation of the power distribution network. Therefore, the research of the active power distribution network optimal scheduling method considering dynamic reconstruction has great practical significance.
Disclosure of Invention
In view of the above, the present invention provides a power distribution network optimal scheduling method and system considering grid dynamic reconfiguration, which can effectively reduce the operation cost of a power distribution network, smooth the voltage level of a power distribution system, and improve the voltage stability of the system.
The invention is realized by adopting the following scheme: a power distribution network optimal scheduling method considering dynamic reconfiguration of a network frame comprises the following steps:
step S1: performing active power distribution network element time sequence modeling;
step S2: with the economy of power distribution network scheduling and the stability of rapid voltage as targets, an active double-layer optimization scheduling model considering network dynamic reconstruction is established, the action condition of a contact switch in a reconstruction period is considered in the upper layer of the model, the active and reactive power output of DGs in each active management period, the switching condition of SVCs and CBs, the OLTC regulation condition and the load reduction condition are considered in the lower layer of the model;
step S3: decimal coding is carried out on the branch in each basic loop, the iteration condition of the net rack population is set, the radial constraint condition of the net rack of the power distribution network is added into the iteration strategy, the active double-layer optimization scheduling model established in the step S2 is solved,
further, step S1 is specifically: establishing a distributed power supply time sequence model, establishing a load time sequence model and establishing an on-load tap changer time sequence model.
Further, step S2 is specifically: in order to realize the cooperative optimization of the upper layer and the lower layer, the optimization targets arranged on the upper layer and the lower layer have the same physical significance and are the operation cost C O And a fast voltage stability FVSI, wherein the upper layer optimization model is established as follows:
(1) the objective function being the total operating cost F 1 Fast voltage stabilization of the system F 2 ：
Figure BDA0002431028560000021
In the formula (I), the compound is shown in the specification,
Figure BDA0002431028560000022
the variables are decided for the upper layer of the network frame,
Figure BDA0002431028560000023
representing the operating cost, FVSI, at time t during the s period s,t Representing static fast voltage stability at time t of the s period,
Figure BDA0002431028560000024
indicating the operating cost, FVSI, of the s period s Static fast voltage stability representing a period s; the upper layer and the lower layer of the model are mutually influenced by the formula (10) and are subjected to coevolution;
(2) constraint conditions are as follows:
condition 1:
Figure BDA0002431028560000031
in the formula, N node Representing the number of nodes in the network;
condition 2: the network has no isolated point and looped network;
the lower optimization model is established as follows:
(1) an objective function:
(a) operating costs
Figure BDA0002431028560000032
Figure BDA0002431028560000033
In the formula (I), the compound is shown in the specification,
Figure BDA0002431028560000034
the cost of the reconstruction is represented as,
Figure BDA0002431028560000035
the cost of the network speed is indicated,
Figure BDA0002431028560000036
the cost of the DG removal is indicated,
Figure BDA0002431028560000037
representing demand side administrative costs;
wherein the content of the first and second substances,
Figure BDA0002431028560000038
Figure BDA0002431028560000039
Figure BDA00024310285600000310
Figure BDA00024310285600000311
in the formula (f) grid Representing the cost of the action of a single reconfiguration switch, and N representing the number of the actuated switches; omega Line Representing the set of lines contained in the reconstructed net frame, f loss The electricity rate per unit of the grid loss is expressed,
Figure BDA00024310285600000312
the active loss on the branch ij is represented, and delta t represents the sampling time length; wherein omega WTG 、Ω PVG Set of installation nodes, f, representing WTG and PVG, respectively WTG,cut 、f PVG,cut Respectively representing the reduction cost of the unit electric quantity WTG and PVG,
Figure BDA0002431028560000041
the active power output of a single WTG and PVG unit at the time of s time period t is shown,
Figure BDA0002431028560000042
respectively representing the number of WTGs and PVGs installed,
Figure BDA0002431028560000043
representing the actual invested quantities of the WTG and PVG at the time of s time t; omega DSM Representing reducible sets of load nodes, f DSM The reduction cost per unit load is expressed,
Figure BDA0002431028560000044
representing the active component of the load at node i at time t of the s period, λ i,s,t Representing a load reduction coefficient of a node i at the time t in the s period;
(b)FVSI：
Figure BDA0002431028560000045
by calculating the static fast voltage stability FVSI of branch ij at time t of s period ij,s,t And determining the voltage stability of the entire system, wherein Q j,s,t Representing the reactive component, X, of node j ij And Z ij Representing reactance and impedance, U, of branch ij, respectively j,s,t Representing the voltage amplitude of a node j, wherein j is a tail end node of the branch ij, and i is a head end node of the branch ij;
(2) constraint conditions are as follows:
(a) and (3) power flow constraint:
Figure BDA0002431028560000046
in the formula, P i 、Q i Respectively representing the equivalent active and reactive power injection, U, of node i i 、U j Representing the voltages at node i and node j, G, respectively ij 、B ij Representing the real and imaginary parts, theta, of the nodal admittance matrix, respectively ij,s,t The phase difference between the node i and the node j at the t-th moment of the branch ij in the s-th reconstruction period is obtained; p is i And Q i Is represented by formula (19):
Figure BDA0002431028560000051
in the formula, p i,s,t And q is i,s,t Respectively representing the active power and the reactive power of the unit equipment at the t moment in the s reconstruction period, n i,s,t The access number of the equipment is represented, the corresponding equipment is represented by superscripts WTG, PVG, MTG, CB and SVC, and the load is represented by superscripts load;
(b) node voltage constraint:
Figure BDA0002431028560000052
in the formula (I), the compound is shown in the specification,Uand
Figure BDA0002431028560000053
respectively representing the upper threshold and the lower threshold of the node voltage;
(c) branch power constraint:
Figure BDA0002431028560000054
in the formula, S ij For the power of the branch ij,
Figure BDA0002431028560000055
is a preset power threshold;
(d) the access quantity of WTG, PVG, MTG, CB and SVC is limited:
Figure BDA0002431028560000056
in the formula (I), the compound is shown in the specification,
Figure BDA0002431028560000057
respectively the access numbers of WTG, PVG, MTG, CB and SVC,
Figure BDA0002431028560000058
respectively the installation quantity of WTG, PVG, MTG, CB and SVC;
(e) OLTC tap tuning range constraints:
Figure BDA0002431028560000059
in the formula, T s,t Which represents the adjustment range of the OLTC tap,T、
Figure BDA0002431028560000061
respectively representing the upper and lower limits of tap adjustment;
(f) WTG and PVG reactive power output constraint:
Figure BDA0002431028560000062
in the formula, S WTG And S PVG Respectively representing rated capacities of WTG and PVG;
(g) active and reactive output constraints of MTG:
Figure BDA0002431028560000063
in the formula, S MTG The rated capacity of the MTG is expressed,
Figure BDA0002431028560000064
the active power output coefficient of the MTG is expressed,
Figure BDA0002431028560000065
representing the reactive power output coefficient of the MTG;
(h) load reduction factor constraint:
Figure BDA0002431028560000066
in the formula (I), the compound is shown in the specification,λ、
Figure BDA0002431028560000067
respectively representing the upper limit and the lower limit of the load reduction coefficient;
(i) upper-level power grid constraint:
Figure BDA0002431028560000068
in the formula (I), the compound is shown in the specification,
Figure BDA0002431028560000069
respectively representing the active and reactive power output of the upper level grid,
Figure BDA00024310285600000610
representing the maximum upper output limit, Δ P, of the upper-level grid gen Representing the maximum ramp rate.
Further, in step S3, the iteration condition of the rack population is specifically:
(1) the number of branches in the topological structure of the power distribution network meets the following requirements: the number of branches is the total node number-the number of basic closed loops;
(2) each basic closed loop can only cut off one branch, and the shared branch part of the adjacent basic closed loops can only cut off one branch at most.
Further, in step S3, solving the active double-layer optimization scheduling model established in step S2 specifically includes:
step SA: planning an upper layer model:
SA 1: let the reconstruction period s be 1,
SA 2: generating an upper-layer net rack initial population, performing radiation type constraint verification on all individuals in the net rack population, and regenerating the individual if the individual cannot pass the verification;
SA 3: sending the upper-layer net frame as an input into the lower layer, and starting the lower-layer model planning;
SA 4: calculating the fitness of the upper layer model by a formula (10), and judging whether a termination condition is met, wherein the termination condition is that the set iteration number upper limit is reached or the fitness convergence precision reaches 10 -6 If yes, entering SA6, otherwise entering SA 5;
SA 5: performing iteration by adopting an MOPSO algorithm according to the conditions of the net rack population to generate a descendant net rack population, and returning to the step SA 3;
SA 6: outputting the optimal net rack in the s-th reconstruction period by adopting a TOPSIS algorithm;
SA 7: and judging whether the optimal net racks in all reconstruction periods have been output, if so, ending, otherwise, making s equal to s +1, and returning to SA 2.
Step SB: planning a lower layer model:
SB 1: making an active management time period t equal to 1;
SB 2: initializing a lower layer population;
SB 3: generating a progeny population by adopting cross and variation operations;
SB 4: inputting a grid structure of an upper layer and distributed power supplies and load data at the tth moment of the s-th reconstruction period to perform load flow calculation;
SB 5: judging whether constraint conditional expressions (18) to (27) are met, if so, entering SB6, otherwise, adding a penalty and entering SB 6; the mode of adding punishment is to set the objective function to be infinite;
SB 6: the fitness value of the lower layer is calculated from equations (12) and (17),
SB 7: judging whether a termination condition is met, wherein the termination condition is that the set iteration number upper limit is reached or the fitness convergence precision reaches 10 -6 If so, entering SB8, otherwise, calculating the crowdedness, and performing fast non-dominated sorting on the population to generate a new parent population, and returning to SB 3;
SB 8: outputting an active management result and a fitness value at the tth moment of the s-th reconstruction period by adopting a TOPSIS algorithm;
SB 9: judging whether active management results at all times in the s-th reconstruction period are output or not, and if so, entering SA4 in the upper-layer model planning; otherwise, go to SB 10;
SB 10: let t be t +1 and return to SB 2.
The invention also provides a power distribution network optimization scheduling system considering the dynamic reconfiguration of the network frame, which comprises a memory and a processor, wherein the memory is stored with a computer program capable of being run by the processor, and the processor can realize the steps of the method when executing the computer program.
The invention also provides a computer-readable storage medium having stored thereon a computer program executable by a processor, the processor being capable of implementing the method steps as described above when executing the computer program.
Compared with the prior art, the invention has the following beneficial effects:
1. the invention takes the radiation type constraint as the iteration condition based on the net rack decimal coding mode of the basic loop, ensures that all the generated net racks meet the requirement, and improves the convergence efficiency of network reconstruction.
2. In the aspect of power distribution network planning, a time sequence model of a distributed power supply (comprising wind power, photovoltaic and a micro gas engine) and a load is established based on a time sequence method, and optimal scheduling of the distributed power supply and an on-load tap changer (OLTC) is realized. Meanwhile, a double-layer optimization model is established, and the optimization target is the economy and the rapid voltage stability (FVSI) of the power distribution network scheduling; the method can effectively reduce the operating cost of the power distribution network, smooth the voltage level of the power distribution system and improve the stability of the system voltage.
Drawings
FIG. 1 is a diagram of the active output power of wind power generation according to an embodiment of the present invention.
Fig. 2 is a graph of active output power of photovoltaic power generation according to an embodiment of the present invention.
Fig. 3 is a graph of the active power of the load of the residents according to the embodiment of the present invention.
Fig. 4 is a graph of the active power of a commercial load according to an embodiment of the present invention.
Fig. 5 is an active power diagram of an industrial load according to an embodiment of the present invention.
Fig. 6 is a topology diagram of a power distribution system according to an embodiment of the invention.
Fig. 7 is a flowchart of solving the planning model according to the embodiment of the present invention.
FIG. 8 is a diagram illustrating the convergence comparison between a conventional binary-coded net frame and the method of the present invention.
Fig. 9 is a schematic diagram of simulation results of the 18 th scenario of four operation states according to the embodiment of the present invention.
Detailed Description
The invention is further explained below with reference to the drawings and the embodiments.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, this embodiment provides a power distribution network optimal scheduling method considering network frame dynamic reconfiguration, including the following steps:
step S1: performing active power distribution network element time sequence modeling;
step S2: with the economy of power distribution network scheduling and the stability of rapid voltage as targets, an active double-layer optimization scheduling model considering network dynamic reconstruction is established, the action condition of a connection switch in a reconstruction period is considered in the upper layer of the model, the active and reactive output of DGs, the switching condition of SVCs and CBs, the OLTC regulation condition and the load reduction condition in each active management period are considered in the lower layer of the model;
step S3: decimal coding is carried out on the branch in each basic loop, the iteration condition of the net rack population is set, the radial constraint condition of the net rack of the power distribution network is added into the iteration strategy, the active double-layer optimization scheduling model established in the step S2 is solved,
in this embodiment, step S1 specifically includes: establishing a distributed power supply time sequence model, establishing a load time sequence model and establishing an on-load tap changer time sequence model. The distributed power supply comprises a Wind Turbine Generator (WTG), a Photovoltaic generator (PVG) and a Micro Turbine Generator (MTG). The active output per unit values of the WTG and PVG are shown in fig. 1 to 2, and the load active output per unit values are shown in fig. 3 to 5.
The method comprises the following specific steps:
the distributed power supply timing model is modeled as follows:
(1) wind power generator
The active power output by a Wind Turbine Generator (WTG) is related to the Wind speed and the cut-in and cut-out Wind speeds of the WTG, and the active power output is as follows:
Figure BDA0002431028560000111
wherein v is ci Indicating cut-in wind speed, v co Indicating cut-out wind speed, v r Which is indicative of the rated wind speed,
Figure BDA0002431028560000112
representing the active power output by the WTG at time t,
Figure BDA0002431028560000113
indicating the power rating of the WTG. The power book of the WTG is shown in fig. 1. The traditional method considers that the WTG can only output active power, and actually, the WTG also has certain reactive power output capacity:
Figure BDA0002431028560000114
wherein the content of the first and second substances,
Figure BDA0002431028560000115
indicating the reactive power output by the WTG at time t,
Figure BDA0002431028560000116
indicating the WTG rated apparent power.
(2) Photovoltaic generator (PVG)
The illumination intensity generally satisfies the Beta distribution, and the photovoltaic generator output and the illumination intensity satisfy the following relationship:
Figure BDA0002431028560000121
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002431028560000122
representing the active output of PVG at time t, I t Indicating the intensity of light at time t, I r Which represents the nominal light intensity of the light,
Figure BDA0002431028560000123
representing the nominal active output of the PVG. The reactive output capability of the PVG is as follows:
Figure BDA0002431028560000124
wherein the content of the first and second substances,
Figure BDA0002431028560000125
representing the reactive power output by the PVG at time t,
Figure BDA0002431028560000126
representing the PVG nominal apparent power.
(3) Miniature gas engine
Active and reactive output of a Micro Turbine (MTG) can be decoupled and adjusted within a certain range, so that the MTG is a controllable distributed power supply:
Figure BDA0002431028560000127
wherein the content of the first and second substances,
Figure BDA0002431028560000128
and
Figure BDA0002431028560000129
respectively representing the active and reactive power output and the rated apparent power of the MTG at the moment t.
The load time sequence model is modeled as follows: the static voltage power function of the load is expressed as follows:
Figure BDA00024310285600001210
wherein
Figure BDA00024310285600001211
And
Figure BDA00024310285600001212
respectively represents the equivalent active and reactive loads of a node i at the moment t, U i,t Represents the per unit value of the actual voltage of the node i at the time t r Representing the rated voltage per unit, the embodiment takes 1, alpha and beta represent the active and reactive characteristic coefficients of the load, respectively, the embodiment takes 0.72 and 2.96,
Figure BDA00024310285600001213
and
Figure BDA00024310285600001214
respectively representing the active and reactive loads at node i at the rated voltage. Demand-side management of loads is achieved by enabling load shedding:
Figure BDA00024310285600001215
wherein Δ P i And Δ Q i Respectively representing the reduction of the active load and the reactive load of the node i, lambda i Indicating the load shedding factor of node i.
The OLTC timing model is modeled as follows:
an On-load tap changer (OLTC) allows the output voltage to be adjusted within a certain range by adjusting the tap position:
ΔU t ＝ΔU OLTC T t (8)
wherein, Delta U t Expressing the per unit value of the voltage variation at time t, Δ U OLTC Represents the voltage per unit value, T, corresponding to one gear t Indicating the tap position at time t.
In this embodiment, step S2 specifically includes: in order to implement the cooperative optimization of the upper layer and the lower layer, the optimization targets set by the upper layer and the lower layer have the same physical meaning, which are the operation cost CO and the fast voltage stability FVSI (FVSI), and the two-layer coordination programming model of this embodiment may be expressed as:
Figure BDA0002431028560000131
in the formula (I), the compound is shown in the specification,
Figure BDA0002431028560000132
the variables are decided for the upper layer of the network frame,
Figure BDA0002431028560000133
representing the underlying active management decision variables. The method comprises the following specific steps:
the upper layer optimization model is established as follows:
(1) the objective function being the total operating cost F 1 Fast voltage stabilization of the system F 2 ：
Figure BDA0002431028560000141
In the formula (I), the compound is shown in the specification,
Figure BDA0002431028560000142
the variables are decided for the upper layer of the network frame,
Figure BDA0002431028560000143
representing the operating cost, FVSI, at time t of the s period s,t Representing static fast voltage stability at time t of the s period,
Figure BDA0002431028560000144
indicating the operating cost, FVSI, of the s period s Static fast voltage stability representing a period s; the upper layer and the lower layer of the model are mutually influenced by the formula (10) and are subjected to coevolution;
(2) constraint conditions are as follows: because the decision variables of the upper layer are only
Figure BDA0002431028560000145
Therefore, the constraint condition is only the radial constraint condition of the power distribution network frame. The radial constraints are as follows:
condition 1:
Figure BDA0002431028560000146
in the formula, N node Representing the number of nodes in the network;
condition 2: the network has no isolated point and looped network;
the lower optimization model is established as follows:
(1) an objective function:
(a) operating costs
Figure BDA0002431028560000147
Figure BDA0002431028560000148
In the formula (I), the compound is shown in the specification,
Figure BDA0002431028560000149
the cost of the reconstruction is represented as,
Figure BDA00024310285600001410
the cost of the network speed is indicated,
Figure BDA00024310285600001411
the cost of the DG removal is indicated,
Figure BDA00024310285600001412
representing demand side administrative costs;
wherein the content of the first and second substances,
Figure BDA00024310285600001413
Figure BDA0002431028560000151
Figure BDA0002431028560000152
Figure BDA0002431028560000153
in the formula (f) grid Representing the cost of the action of a single reconfiguration switch, and N representing the number of switches of the action; omega Line Representing the set of lines contained in the reconstructed net frame, f loss The electricity rate per unit of the grid loss is expressed,
Figure BDA0002431028560000154
representing the active loss on branch ij, Δt the sampling time is shown, and the sampling time is 1 hour in the embodiment; wherein ΩWTG 、 ΩPVG Set of installation nodes, f, representing WTG and PVG, respectively WTG,cut 、f PVG,cut Respectively representing the reduction cost of the unit electric quantity WTG and PVG,
Figure BDA0002431028560000155
Figure BDA0002431028560000156
the active power output of a single WTG and PVG unit at the time of s time period t is shown,
Figure BDA0002431028560000157
respectively representing the number of WTGs and PVGs installed,
Figure BDA0002431028560000158
representing the actual invested quantities of the WTG and PVG at the time of s time t; Ω DSM representing reducible sets of load nodes, f DSM The reduction cost per unit load is expressed,
Figure BDA0002431028560000159
representing the active component, λ, of the load at node i at time t during s i,s,t Representing a load reduction coefficient of a node i at the time t in the s period;
(b)FVSI：
Figure BDA00024310285600001510
by calculating the static fast voltage stability FVSI of branch ij at time t of s period ij,s,t And determining the voltage stability of the entire system, wherein Q j,s,t Representing the reactive component, X, of node j ij And Z ij Representing reactance and impedance, U, of branch ij, respectively j,s,t Representing the voltage amplitude of a node j, wherein j is a tail end node of the branch ij, and i is a head end node of the branch ij;
(2) constraint conditions are as follows:
(a) and (3) power flow constraint:
Figure BDA0002431028560000161
in the formula, P i Qi respectively represent the equivalent active and reactive power injection, U, of the node i i 、U j Representing the voltages of node i and node j, G, respectively ij 、B ij Representing the real and imaginary parts, theta, of the nodal admittance matrix, respectively ij,s,t The phase difference between the node i and the node j at the t-th moment of the branch ij in the s-th reconstruction period is obtained; p i And Q i Is represented by formula (19):
Figure BDA0002431028560000162
in the formula, p i,s,t And q is i,s,t Respectively representing the active power and the reactive power of the unit equipment at the t moment in the s reconstruction period, n i,s,t The access number of the equipment is represented, the corresponding equipment is represented by superscripts WTG, PVG, MTG, CB and SVC, and the load is represented by superscripts load;
(b) node voltage constraint:
Figure BDA0002431028560000163
in the formula (I), the compound is shown in the specification,Uand
Figure BDA0002431028560000164
respectively representing the upper threshold and the lower threshold of the node voltage; according to the requirement of the technical guideline for planning and designing the distribution network, the voltage deviation of the medium-voltage distribution network cannot exceed 7 percent of the rated value, therefore,Utaking out the solution of 0.93pu,
Figure BDA0002431028560000165
1.07pu was taken.
(c) Branch power constraint:
Figure BDA0002431028560000166
in the formula, S ij For the power of the branch ij is,
Figure BDA0002431028560000171
is a preset power threshold;
(d) the access quantity of WTG, PVG, MTG, CB and SVC is limited:
Figure BDA0002431028560000172
in the formula (I), the compound is shown in the specification,
Figure BDA0002431028560000173
respectively the access numbers of WTG, PVG, MTG, CB and SVC,
Figure BDA0002431028560000174
respectively the installation quantity of WTG, PVG, MTG, CB and SVC;
(e) OLTC tap tuning range constraints:
Figure BDA0002431028560000175
in the formula, T s,t Which represents the adjustment range of the OLTC tap,T、
Figure BDA0002431028560000176
respectively representing the upper and lower limits of tap adjustment;
(f) WTG and PVG reactive power output constraint:
Figure BDA0002431028560000177
in the formula, S WTG And S PVG Respectively representing rated capacities of WTG and PVG; in general, WTGs and PVGs do not absorb reactive power and therefore operate in the first quadrant of the output curve.
(g) Active and reactive output constraints of MTG:
Figure BDA0002431028560000181
in the formula, S MTG The rated capacity of the MTG is expressed,
Figure BDA0002431028560000182
the active power output coefficient of the MTG is expressed,
Figure BDA0002431028560000183
representing the reactive power output coefficient of the MTG; the MTG can realize active and reactive decoupling control, so that an active output coefficient is set
Figure BDA0002431028560000184
And coefficient of reactive power output
Figure BDA0002431028560000185
And controlling the active and reactive outputs of the MTG.
(h) Load reduction factor constraint:
Figure BDA0002431028560000186
in the formula (I), the compound is shown in the specification,λ、
Figure BDA0002431028560000187
respectively representing the upper limit and the lower limit of the load reduction coefficient;
(i) upper-level power grid constraint:
Figure BDA0002431028560000188
in the formula (I), the compound is shown in the specification,
Figure BDA0002431028560000189
respectively representing the active and reactive power output of the upper level grid,
Figure BDA00024310285600001810
representing the maximum upper output limit, Δ P, of the upper-level grid gen Representing the maximum ramp rate.
In this embodiment, in step S3, the iteration condition of the rack population is specifically:
(1) according to the requirement of the radiation type, the branch number in the topological structure of the power distribution network is easy to know and meets the following requirements: the number of branches is the total node number-the number of basic closed loops;
(2) in addition, because the loop-free and island-free requirements need to be met, each basic closed loop can only be opened by one branch, and the shared branch part of the adjacent basic closed loops can only be opened by one branch at most.
The N basic closed loops for the distribution network are represented as follows:
Figure BDA0002431028560000191
wherein the number of branches of each basic loop L is n 1 ，n 2 ，……，n N Of branch lThe superscript is the decimal coding of the branch, but the branch itself still adopts binary coding; namely, for any branch, l is belonged to {0,1 }; when the common branch is not considered, the following constraints are applied to the basic closed loop individuals:
Figure BDA0002431028560000192
consider further an adjacent closed loop L i And L j Common branch L of a closed loop ij The following were used:
Figure BDA0002431028560000193
common branch L ij At most, only one branch can be disconnected, i.e.
sum(L ij )＝n ij ；
Or satisfy
sum(L ij )＝n ij -1
Coding and iteration are carried out on the net rack population according to the conditions, so that the requirement of the radial constraint condition can be met, and the upper-layer optimization is converted into the unconstrained optimization.
Specifically, the present embodiment adopts an IEEE 33 node power distribution system as an example, and a topological diagram of the power distribution system is shown in fig. 6. For the IEEE 33 node system shown in this embodiment, 32 branches must be present in the net rack to satisfy the radial constraint. Meanwhile, the requirements of no island and no loop in the net rack are also required to be met. As can be seen from fig. 6, there are 5 basic closed loops in the IEEE 33 node system, so that only one branch needs to be opened in each basic closed loop, and at most one branch can be opened in the shared branch portion of the adjacent basic closed loops. The generated net rack population can be ensured to satisfy the radial constraint condition according to the rule, so that the upper-layer optimization is changed into unconstrained optimization.
Based on the above principle, the present invention proposes a decimal coding method based on a basic closed loop. The specific coding method is as follows:
TABLE 1 encoding method of basic closed loop
Figure BDA0002431028560000201
Taking basic loop 1 and basic loop 2 as examples, the common branches are branches 3, 4 and 5. According to the above evolution rule, if branch 3 (common branch) in the basic loop 1 is disconnected, only other branches except for branches 3, 4, and 5 can be disconnected in the basic loop 2; if basic loop 1 disconnects leg 6 (the non-shared leg), then basic loop 2 may disconnect any leg. In the process of the grid variable evolution, all adjacent basic loops adopt the same evolution mode. Therefore, each generated net rack can meet the radial constraint in the evolution process of the net rack population, the upper-layer optimization is changed into unconstrained optimization, and the convergence rate of the algorithm is greatly improved.
In this embodiment, in step S3, the solving of the active double-layer optimization scheduling model established in step S2 specifically includes:
step SA: planning an upper layer model:
SA 1: let the reconstruction period s be 1,
SA 2: generating an upper-layer net rack initial population, performing radiation type constraint verification on all individuals in the net rack population, and regenerating the individual if the individual cannot pass the verification;
SA 3: sending the upper-layer net frame as an input into the lower layer, and starting the lower-layer model planning;
SA 4: calculating the fitness of the upper layer model by a formula (10), and judging whether a termination condition is met, wherein the termination condition is that a preset iteration number upper limit is reached, or the fitness convergence precision reaches 10 -6 If yes, entering SA6, otherwise entering SA 5;
SA 5: performing iteration by adopting an MOPSO algorithm according to the conditions of the net rack population to generate a descendant net rack population, and returning to the step SA 3;
SA 6: outputting the optimal net rack in the s-th reconstruction period by adopting a TOPSIS algorithm;
SA 7: and judging whether the optimal net racks in all reconstruction periods have been output, if so, ending, otherwise, making s equal to s +1, and returning to SA 2.
Step SB: planning a lower layer model:
SB 1: making an active management time period t equal to 1;
SB 2: initializing a lower layer population;
SB 3: generating a progeny population by adopting cross and variation operations;
SB 4: inputting a grid structure of an upper layer and distributed power supplies and load data at the tth moment of the s-th reconstruction period to perform load flow calculation;
SB 5: judging whether constraint conditional expressions (18) to (27) are met, if so, entering SB6, otherwise, adding a penalty and entering SB 6; the mode of adding penalty is to set the objective function to be infinite;
SB 6: the fitness value of the lower layer is calculated from equations (12) and (17),
SB 7: judging whether a termination condition is met, wherein the termination condition is that the upper limit of iteration times is reached or the convergence precision of fitness reaches 10 -6 If so, entering SB8, otherwise, calculating the congestion degree, performing rapid non-dominated sorting on the population (wherein the calculation of the congestion degree and the rapid non-dominated sorting belong to the existing algorithm, which is not described in detail in the invention), generating a new parent population, and returning to SB 3;
SB 8: outputting an active management result and a fitness value at the tth moment of the s-th reconstruction period by adopting a TOPSIS algorithm;
SB 9: judging whether active management results at all times in the s-th reconstruction period are output or not, and if so, entering SA4 in the upper-layer model planning; otherwise, go to SB 10;
SB 10: let t be t +1 and return to SB 2.
Preferably, the present embodiment uses NDX operator to perform the crossover operation to generate the offspring population.
Figure BDA0002431028560000221
Wherein x is 1j And x 2j Representing any two of the parent individuals,
Figure BDA0002431028560000222
and
Figure BDA0002431028560000223
represents the generated individual offspring, N (0,1) represents a normal distribution function with a mean value of 0 and a standard deviation of 1.μ represents an arbitrary random number within the interval (0, 1).
And (3) improving the diversity degree of the population by adopting polynomial variation, wherein the variation operation is as follows:
Figure BDA0002431028560000224
where η represents a polynomial variation shape parameter and μ represents an arbitrary random number within the (0,1) interval.
The embodiment also provides a power distribution network optimization scheduling system considering network frame dynamic reconfiguration, which includes a memory and a processor, wherein the memory stores a computer program capable of being executed by the processor, and the processor can implement the method steps as described above when executing the computer program.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program executable by a processor, the processor being capable of implementing the method steps as described above when executing the computer program.
In this embodiment, the WTG installation nodes are nodes 7 and 24, the installation numbers are 5 groups, the PVG installation nodes are nodes 25 and 30, the installation numbers are 5 groups, the MTG installation nodes are nodes 8 and 32, the installation numbers are 5 groups, the total network load is set to be 1.5 times of the reference value, that is, the network load is 7.63MW +3.82Mvar, the wind power and photovoltaic reduction cost is 1 yuan/kWh, the network loss cost is 0.5 yuan/kWh, the rated power factor of the MTG is 0.9, the switching cost is 2 yuan/piece of OLTC and has 9 gears, the voltage regulated by each gear is 0.0125pu, therefore, the voltage adjustable range is 1 ± 4 × 0.0125pu, the nodes 15, 18 and 21 are reducible load nodes, the load reduction coefficient range is 0 to 0.15, the load reduction cost is 1 yuan/kWh, the installation nodes are nodes 7, CB 11, 12 and 31, the unit capacity is 50kvar, and the maximum number of the access group is 5, the SVC access node is node 19, the unit SVC capacity is 20kvar, and the maximum access number is 5 groups. The specific solving flow is shown in fig. 7.
Since the model of the embodiment is a multi-objective optimization model, the upper layer adopts an improved mopso (multi-objective partial sweep optimization) algorithm to perform calculation, learning factors are set to 1.49 and 1.49 respectively, and inertial weight is set to 0.8. The lower layer adopts an improved NSGA II algorithm, the cross operation of the algorithm is improved by using an NDX operator, and the population diversity is improved by using a polynomial mutation operator. The algorithm parameters are set as follows: the number of initial populations is 50, the maximum iteration number is 50, the cross rate is 0.9, the variation rate is 0.1, and the distribution index eta of polynomial variation is 5.
In order to verify the effectiveness of the basic loop-based decimal coding method provided by the embodiment, the double-layer optimization model provided by the embodiment is simplified, and active management of the power distribution network is not considered, namely the model provided by the invention is degenerated into a single-layer model. The convergence of the conventional binary coded net frame and the method of the present invention is shown in fig. 8.
As can be seen from the attached figure 8, the convergence rate of the grid coding algorithm provided by the invention is high, according to the results of multiple simulation experiments, the calculation results can be converged in about the 15 th generation by using the grid population coding and evolution method provided by the invention, and the reconstructed average convergence time is 1.19s on the premise of not considering active management means. The traditional method has low convergence speed and cannot stably converge, and the invention carries out 10 experiments on the traditional net rack coding method. The evolution algebra is set to be 100, and the traditional grid coding method cannot ensure stable convergence within the specified evolution times, wherein only 5 times of calculation results are consistent with the results obtained by the method provided by the invention. According to the 10 experimental results, the traditional method converges at the 65 th generation at the fastest time, and the convergence time is 5.63 s.
The main reason that the traditional method cannot stably converge is that the traditional method generates a net rack population in the evolution process, then performs radiation constraint verification on the population, and has no limitation when generating the net rack, so that a large number of invalid net racks are generated, the convergence speed is very slow, and the convergence result depends on the setting of an initial population. The invention takes the radial constraint as one of the conditions of population evolution, and can ensure that any population generated in the evolution process meets the radial constraint condition without generating invalid solutions, so the method provided by the invention has stable and quick convergence.
This example divides a typical day into 4 reconstruction periods, 1: 00-6: 00. 7: 00-10: 00. 11: 00-20: 00 and 21: 00-24: 00. at the beginning of each reconstruction period, the rack is reconstructed, and during one reconstruction period, the rack remains unchanged. The net rack simulation results of the method of the invention are shown in table 2.
TABLE 2 dynamic reconfiguration of the net frame according to the method of the invention
Figure BDA0002431028560000241
Figure BDA0002431028560000251
The calculation results of the indices are shown in table 3, with and without reconstruction taken into account.
TABLE 3 optimization results of the process of the invention
Figure BDA0002431028560000252
As can be seen from the results in table 3, the total running cost at the time of reconstruction was 64.84 ten thousand yuan, whereas the total running cost at the time of reconstruction was 92.88 ten thousand yuan, the annual running cost was reduced by 28.04 ten thousand yuan, and the annual running cost was reduced by 43.24%.
When network reconfiguration is considered, a reconfiguration cost of 2.34 ten thousand dollars is incurred. When the reconstruction is considered, the line loss cost and the load reduction cost are slightly reduced by 1.59 percent and 2.09 percent respectively; but in terms of the electricity abandonment cost of the distributed power supply, the cost of abandoning wind and light is 22.52 ten thousand yuan when reconstruction is considered, and the cost is 52.19 ten thousand yuan when reconstruction is not considered. As can be seen from the cost calculation result of wind curtailment and light curtailment, the power curtailment cost of the reconstructed distributed power supply is greatly reduced, and 29.67 ten thousand yuan is reduced. Namely, after the reconstruction is considered, the electricity abandoning phenomenon of the DG is greatly reduced, so that the consumption rate of the distributed power supply is greatly improved.
Further, considering that the FVSI of the distribution network at the time of reconstruction is 1.41 and not considering that the FVSI of the distribution network at the time of reconstruction is 1.60, the smaller this value is, the more stable the system is according to the definition of FVSI. Therefore, the invention adopts the reciprocal of FVSI to calculate the improvement level of the system voltage performance, and after the reconstruction is considered, the voltage stability of the system is improved by 11.88%.
In summary, although the network reconfiguration brings about a certain reconfiguration cost, the network reconfiguration is beneficial to the consumption of the distributed power supply. And the voltage stability of the network can be improved to a certain extent while the permeability of the distributed power supply is improved. Therefore, the dynamic reconfiguration of the power distribution network is also a very important adjusting means, and the economy and the reliability of the power distribution network can be greatly improved by matching with the active management technology of the power distribution network.
In order to further analyze the improvement of the method provided by the invention on the system voltage level, 4 operation states are set and simulation is carried out. Operation state 1: original system; operation state 2: considering network reconstruction on the basis of an original network; operating state 3: consider proactive management but not network reconfiguration; operation state 4: while considering network reconfiguration and proactive management. The four operating states are at 18: the simulation results at time 00 are shown in fig. 9.
According to the result of the operation state 1 in fig. 9, the voltage fluctuation of the original system is large, the maximum voltage difference is 0.0967pu, the lowest voltage of the system appears at the node 17, the lowest voltage is 0.9033pu and is far lower than the specified lower limit value, and in addition, the voltages of a plurality of nodes in the system are lower than the lower voltage limit of 0.93 pu. Therefore, the reliability of the original system voltage is lower. According to the result of the operation state 2 in fig. 9, after the power distribution network adopts the reconstruction means, the voltage level is raised, the maximum voltage difference is 0.0856pu, the minimum voltage is 0.9144pu, and the network reconstruction is improved by 0.0111pu compared with the operation state 1, which shows that the network reconstruction is helpful for raising the voltage level of the power distribution network. According to the result of the operation state 3 in fig. 9, when only the active management means is considered, due to the access of the distributed power sources, the distributed power sources can effectively reduce the equivalent load of the distribution network, and meanwhile, the reactive output capability of the distributed power sources can effectively support the system voltage, so that the voltage level of the distribution network is greatly improved compared with the operation state 1. However, since the network structure is the same as the original network, the voltage level has the same change trend as the original network, and since the nodes 16, 17, and 18 are located at the end of the longer branch and there is no distributed power supply nearby, sufficient voltage support cannot be provided, and therefore the voltage levels of the three nodes are still lower than the lower voltage limit of 0.93 pu. According to the result of the operation state 4 in fig. 9, after the system adopts two control means of dynamic reconfiguration and active management, the distribution of voltage becomes more stable due to the change of the network structure, the maximum voltage of the distribution network is 0.9625pu, the minimum voltage is 0.9511pu, the voltage difference of the distribution network is 0.0114pu, and the voltage fluctuation level is smaller than that in the first three operation states. In addition, after dynamic reconfiguration and active management are considered, the voltage of all nodes is higher than the lower voltage limit of 0.93pu, and the requirement of the power distribution network on the voltage is met.
The foregoing is directed to preferred embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow. However, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present invention are within the protection scope of the technical solution of the present invention.

Claims (6)

1. A power distribution network optimal scheduling method considering network frame dynamic reconstruction is characterized by comprising the following steps:
step S1: performing active power distribution network element time sequence modeling;
step S2: with the economy of power distribution network scheduling and the stability of rapid voltage as targets, an active double-layer optimization scheduling model considering network dynamic reconstruction is established, the action condition of a connection switch in a reconstruction period is considered in the upper layer of the model, the active and reactive output of DGs, the switching condition of SVCs and CBs, the OLTC regulation condition and the load reduction condition in each active management period are considered in the lower layer of the model;
step S3: decimal coding is carried out on the branch in each basic loop, the iteration condition of the net rack population is set, the radiation type constraint condition of the net rack of the power distribution network is added into the iteration strategy, and the active double-layer optimization scheduling model established in the step S2 is solved;
step S2 specifically includes: in order to realize the cooperative optimization of the upper layer and the lower layer, the optimization targets arranged on the upper layer and the lower layer have the same physical significance and are the operation cost C O And a fast voltage stability FVSI, wherein,
the upper layer optimization model is established as follows:
(1) the objective function being the total operating cost F 1 Fast voltage stabilization of the system F 2 ：
Figure FDA0003694515070000011
In the formula (I), the compound is shown in the specification,
Figure FDA0003694515070000012
the variables are decided for the upper layer of the network frame,
Figure FDA0003694515070000013
representing the operating cost, FVSI, at time t during the s period s,t Representing static fast voltage stability at time t of the s period,
Figure FDA0003694515070000014
indicating the operating cost, FVSI, of the s period s Static fast voltage stability representing a period s; the upper layer and the lower layer of the model are mutually influenced by the formula (10) and are subjected to coevolution;
(2) constraint conditions are as follows:
condition 1:
Figure FDA0003694515070000021
in the formula, N node Representing the number of nodes in the network;
condition 2: the network has no isolated point and looped network;
the lower optimization model is established as follows:
(1) an objective function:
(a) operating costs
Figure FDA0003694515070000022
Figure FDA0003694515070000023
In the formula (I), the compound is shown in the specification,
Figure FDA0003694515070000024
the cost of the reconstruction is represented as,
Figure FDA0003694515070000025
the cost of the network speed is indicated,
Figure FDA0003694515070000026
the cost of the DG removal is indicated,
Figure FDA0003694515070000027
representing demand side administrative costs;
wherein the content of the first and second substances,
Figure FDA0003694515070000028
Figure FDA0003694515070000029
Figure FDA00036945150700000210
Figure FDA00036945150700000211
in the formula (f) grid Representing the cost of the action of a single reconfiguration switch, and N representing the number of switches of the action; omega Line Representing the set of lines contained in the reconstructed net frame, f loss The electricity rate per unit of the grid loss is expressed,
Figure FDA0003694515070000031
the active loss on the branch ij is represented, and delta t represents the sampling time length; wherein omega WTG 、 ΩPVG Set of installation nodes, f, representing WTG and PVG, respectively WTG,cut 、f PVG,cut Respectively representing the reduction cost of the unit electric quantity WTG and PVG,
Figure FDA0003694515070000032
the active power output of a single WTG and PVG unit at the time of s time period t is shown,
Figure FDA0003694515070000033
Figure FDA0003694515070000034
respectively representing the number of WTGs and PVGs installed,
Figure FDA0003694515070000035
representing the actual invested quantities of the WTG and PVG at the time of s time t; omega DSM Representing reducible sets of load nodes, f DSM The reduction cost per unit load is expressed,
Figure FDA0003694515070000036
representing the active component of the load at node i at time t of the s period, λ i,s,t Representing a load reduction coefficient of a node i at the time t in the s period;
(b)FVSI：
Figure FDA0003694515070000037
by calculating the static fast voltage stability FVSI of the branch ij at s time t ij,s,t And determining the voltage stability of the entire system, wherein Q j,s,t Representing the reactive component, X, of node j ij And Z ij Representing reactance and impedance, U, of branch ij, respectively j,s,t Representing the voltage amplitude of a node j, wherein j is a tail end node of the branch ij, and i is a head end node of the branch ij;
(2) constraint conditions are as follows:
(a) and (3) power flow constraint:
Figure FDA0003694515070000038
in the formula, P i 、Q i Respectively representing the equivalent active and reactive power injection, U, of node i i 、U j Representing the voltages of node i and node j, G, respectively ij 、B ij Representing the real and imaginary parts, theta, of the nodal admittance matrix, respectively ij,s,t The phase difference between the node i and the node j at the t-th moment of the branch ij in the s-th reconstruction period is obtained; p i And Q i Is represented by formula (19):
Figure FDA0003694515070000041
in the formula, p i,s,t And q is i,s,t Respectively representing the active power and the reactive power of the unit equipment at the t moment in the s reconstruction period, n i,s,t The access number of the equipment is represented, the corresponding equipment is represented by superscripts WTG, PVG, MTG, CB and SVC, and the load is represented by superscripts load;
(b) node voltage constraint:
Figure FDA0003694515070000042
in the formula, U and
Figure FDA0003694515070000043
respectively representing the upper threshold and the lower threshold of the node voltage;
(c) branch power constraint:
Figure FDA0003694515070000044
in the formula, S ij For the power of the branch ij,
Figure FDA0003694515070000045
is a preset power threshold;
(d) the access quantity of WTG, PVG, MTG, CB and SVC is limited:
Figure FDA0003694515070000046
in the formula (I), the compound is shown in the specification,
Figure FDA0003694515070000047
respectively the access numbers of WTG, PVG, MTG, CB and SVC,
Figure FDA0003694515070000048
respectively the installation quantity of WTG, PVG, MTG, CB and SVC;
(e) OLTC tap tuning range constraints:
Figure FDA0003694515070000051
in the formula (I), the compound is shown in the specification,T s,t represents the adjustment range of the OLTC tap, T,
Figure FDA0003694515070000052
Respectively representing the upper and lower limits of tap adjustment;
(f) WTG and PVG reactive power output constraint:
Figure FDA0003694515070000053
in the formula, S WTG And S PVG Respectively representing rated capacities of WTG and PVG;
(g) active and reactive output constraints of MTG:
Figure FDA0003694515070000054
in the formula, S MTG The rated capacity of the MTG is expressed,
Figure FDA0003694515070000055
the active power output coefficient of the MTG is expressed,
Figure FDA0003694515070000056
representing the reactive power output coefficient of the MTG;
(h) load reduction factor constraint:
Figure FDA0003694515070000057
in the formula (I), the compound is shown in the specification,λ、
Figure FDA0003694515070000058
respectively representing the upper limit and the lower limit of the load reduction coefficient;
(i) upper-level power grid constraint:
Figure FDA0003694515070000061
in the formula (I), the compound is shown in the specification,
Figure FDA0003694515070000062
respectively representing the active and reactive power output of the upper level grid,
Figure FDA0003694515070000063
representing the maximum upper output limit, Δ P, of the upper-level grid gen Representing the maximum ramp rate.
2. The method for optimizing and scheduling the power distribution network considering the dynamic reconfiguration of the network frame according to claim 1, wherein the step S1 is specifically: establishing a distributed power supply time sequence model, establishing a load time sequence model and establishing an on-load tap changing transformer time sequence model.
3. The power distribution network optimal scheduling method considering grid dynamic reconstruction according to claim 1, wherein in step S3, the iteration condition of the grid population is specifically:
(1) the number of branches in the topological structure of the power distribution network meets the following requirements: the number of branches is the total node number-the number of basic closed loops;
(2) each basic closed loop can only cut off one branch, and the shared branch part of the adjacent basic closed loops can only cut off one branch at most.
4. The power distribution network optimal scheduling method considering grid dynamic reconfiguration according to claim 1, wherein in step S3, solving the active double-layer optimal scheduling model established in step S2 specifically comprises:
step SA: planning an upper layer model:
SA 1: let the reconstruction period s be 1,
SA 2: generating an upper-layer net rack initial population, performing radiation type constraint verification on all individuals in the net rack population, and regenerating the individual if the individual cannot pass the verification;
SA 3: sending the upper-layer net frame as an input into the lower layer, and starting the lower-layer model planning;
SA 4: calculating the fitness of the upper layer model through a formula (10), and judging whether a termination condition is met, if so, entering SA6, otherwise, entering SA 5;
SA 5: performing iteration by adopting an MOPSO algorithm according to the conditions of the net rack population to generate a descendant net rack population, and returning to the step SA 3;
SA 6: outputting the optimal net rack in the s-th reconstruction period by adopting a TOPSIS algorithm;
SA 7: judging whether the optimal net racks in all reconstruction periods have been output, if so, ending, otherwise, making s equal to s +1, and returning to SA 2;
step SB: planning a lower layer model:
SB 1: making an active management time period t equal to 1;
SB 2: initializing a lower layer population;
SB 3: generating a progeny population by adopting cross and variation operations;
SB 4: inputting a grid structure of an upper layer and distributed power supplies and load data at the tth moment of the s-th reconstruction period to perform load flow calculation;
SB 5: judging whether constraint conditional expressions (18) to (27) are met, if so, entering SB6, otherwise, adding a penalty and entering SB 6; the mode of adding penalty is to set the objective function to be infinite;
SB 6: the fitness value of the lower layer is calculated from equations (12) and (17),
SB 7: judging whether a termination condition is met, if so, entering SB8, otherwise, calculating the crowdedness, performing rapid non-dominated sorting on the population, generating a new parent population, and returning to SB 3;
SB 8: outputting an active management result and a fitness value at the tth moment of the s-th reconstruction period by adopting a TOPSIS algorithm;
SB 9: judging whether active management results at all times in the s-th reconstruction period are output or not, and if so, entering SA4 in the upper-layer model planning; otherwise, go to SB 10;
SB 10: let t be t +1 and return to SB 2.
5. An optimized dispatching system for a power distribution network considering dynamic reconfiguration of network frames, which is characterized by comprising a memory and a processor, wherein the memory is stored with a computer program capable of being executed by the processor, and the processor can realize the method steps of any one of claims 1 to 4 when executing the computer program.
6. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which can be executed by a processor, which, when executing the computer program, is able to carry out the method steps of any of claims 1 to 4.
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