CN111224397A - Configuration method for position and constant volume of distributed power supply connected to power distribution network - Google Patents

Configuration method for position and constant volume of distributed power supply connected to power distribution network Download PDF

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CN111224397A
CN111224397A CN202010062255.6A CN202010062255A CN111224397A CN 111224397 A CN111224397 A CN 111224397A CN 202010062255 A CN202010062255 A CN 202010062255A CN 111224397 A CN111224397 A CN 111224397A
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distribution network
branch
representing
node
constraint
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黄宸希
孙保华
韩韬
吴雪琼
王必恒
席旸旸
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NARI Group Corp
NARI Nanjing Control System Co Ltd
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NARI Nanjing Control System 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
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A30/00—Adapting or protecting infrastructure or their operation
    • Y02A30/60—Planning or developing urban green infrastructure

Abstract

The invention discloses a method for configuring the position and constant volume of a distributed power supply access power distribution network. And then, finding the optimal position and the optimal capacity of the distributed power supply connected to the power distribution network by applying a correlation algorithm. Finally, the network loss of the power distribution network is reduced, and the voltage quality is improved.

Description

Configuration method for position and constant volume of distributed power supply connected to power distribution network
Technical Field
The invention relates to a method for accessing a distributed power supply to a power grid, in particular to a configuration method for the position and constant volume of the distributed power supply accessed to the power grid.
Background
With the development of national economy, the demand of electric power is also increasing. In recent years, the electrical load in China has increased dramatically. In order to cope with the increasing power consumption, on one hand, the installed capacity may need to be increased, and on the other hand, the electric energy needs to be saved to realize the sustainable development of energy and social economy, so that the utilization rate of the energy is improved, and the pollution to the environment is reduced. The distributed power supply has the characteristics of flexibility, dispersion, high efficiency, cleanness and environmental protection, greatly reduces the cost and the loss of a power transmission network, can be used for peak shaving or supplying power to remote users in order to meet specific requirements of a system or users, and can also improve the reliability of power supply of the system.
In recent years, with the development of science and technology, the power generation cost is reduced year by year, the operation control technology is increasingly improved, and the possibility is provided for large-scale distributed power supply grid connection. The distributed power supply is connected to the power distribution network, so that the power transmission cost can be effectively reduced, the electric energy quality can be improved, and the future development trend of new energy is realized. However, the related technology of the distributed electric access power distribution network is not mature, how the distributed power supply is scientifically accessed to the power distribution network is always the key point of attention of people, and the distributed power supply is reasonable in configuration, so that the construction investment can be reduced, the energy utilization efficiency can be improved, the network loss of the power distribution network can be reduced, and the node voltage can be improved. However, if the configuration is not reasonable, the power loss may be increased, which may cause the voltage of some nodes in the network to drop or cause overvoltage. Therefore, the method has important research significance for optimizing the distribution network containing the distributed power supply and has profound influence on the future development of the power system.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems, the invention provides a configuration method for the position and constant volume of a distributed power supply connected to a power distribution network, which can reduce the loss of the power distribution network and improve the voltage quality of the power distribution network.
The technical scheme is as follows: the technical scheme adopted by the invention is a configuration method for the position and constant volume of a distributed power supply connected to a power distribution network, which comprises the following steps:
(1) accessing a distribution network structure according to a distributed power supply, calculating the loss of the distribution network through the active power and the reactive power of branches of the structure, and establishing a configuration mathematical model; the configuration mathematical model is as follows:
Figure BDA0002373522260000011
wherein i represents the ith branch of the power distribution network, L is the total number of the branches of the power distribution network, and riIs a branch resistance; piAnd QiRespectively the active power and the reactive power of the flowing branch; k is a radical ofiRepresenting the state of the switch on the branch, 0 when open, 1, V when closediIs the branch end voltage;
(2) establishing constraint conditions for the configuration mathematical model;
(3) and solving the optimal solution of the configuration mathematical model by adopting a particle swarm algorithm to obtain the optimal distributed power supply position and capacity configuration.
The constraint conditions in the step (2) include node voltage constraint, line current constraint, power flow constraint, branch capacity constraint and DG output constraint, and specifically include:
(21) node voltage constraint
Uimin≤Ui≤Uimax
Where Ui denotes the voltage of the ith load node, UiminAnd UimaxRespectively representing the upper limit and the lower limit of the voltage of the load node;
(22) line current confinement
Ij＜Ijmax
Wherein IjRepresenting the current of the j-th line, IjmaxRepresents the maximum current allowed on the jth line;
(23) and (3) power flow constraint: i.e. the power flow equation must be satisfied
Figure BDA0002373522260000021
Wherein P isisFor active injection power of node i, QisReactive injection power, U, for node iiIs the voltage amplitude of node i, UjIs the voltage amplitude of node j, GijFor the conductance of the branch between nodes i and j, BijFor branch susceptance, θ, between nodes i and jijRepresenting the difference in the voltage phase at node i and node j.
(24) Branch capacity constraint
Sj≤Sjmax
Wherein SjTo branch capacity, SjmaxMaximum capacity allowed for the tributary for transmission;
(25) DG output constraint
PDG，imin≤PDG，i≤PDG，imax
Wherein P isDG，iDenotes the capacity, P, of the ith distributed power supplyDG，iminIs the minimum value of the ith distributed power source capacity, PDG，imaxIs the maximum value of the ith distributed power source capacity.
The solving and configuring of the optimal solution of the mathematical model by adopting the particle swarm algorithm in the step (3) comprises the following steps:
(31) setting parameters of a particle swarm algorithm, including the swarm scale, the maximum iteration times and the learning factors of the particle swarm, reading original data of the power distribution network, and obtaining branch information of related nodes;
(32) randomly generating an initial particle population, randomly generating M particles to form the initial population and endowing the initial population with an initial speed;
(33) carrying out load flow calculation on a power distribution network corresponding to each particle, calculating the particle fitness to obtain an individual optimal solution of the particles, taking the particles with the maximum fitness as a global optimal solution, and setting the current iteration time t to be 1;
(34) judging whether the maximum iteration number T is reachedmaxIf so, outputting the optimal solution of the current group, and ending;
(35) updating the speed and the position of the particles according to an updating formula, updating the individual extreme value and the global optimal solution, and judging;
(36) and (5) continuing to calculate and solve in the step (34) when the iteration time t is t + 1.
Wherein, the updating formula in the step (35) is:
Figure BDA0002373522260000031
wherein v isidRepresenting the component of the particle i's flying velocity vector in the d-dimension, ω representing the inertia factor, ωmaxAnd ωminMaximum and minimum ω values, respectively; c1And C2Is the learning factor of the learning factor,
Figure BDA0002373522260000032
represents the optimal solution that the particle i has experienced,
Figure BDA0002373522260000033
representing the optimal solution of the population; x is the number ofidRepresenting the flight position vector of the particle i in the d-component, TmaxRepresenting the maximum number of iterations and t representing the number of current iterations.
Has the advantages that: compared with the scheme that distributed power sources are configured nearby according to the geographic positions of loads in the prior art, the method disclosed by the invention has the advantages that the important indexes of the power distribution network are selected, the configuration mathematical model reflecting the loss of the power distribution network according to the structure that the distributed power sources are accessed into the power distribution network is established, the selection method of the positions of the distributed power sources accessed into the power distribution network and the constant volume is designed, the loss of the power distribution network is reduced, the optimal solution of the data model is calculated by utilizing a particle swarm algorithm, and the optimal position of the distributed power sources accessed into the power.
Drawings
FIG. 1 is a flow chart of a particle swarm algorithm according to the present invention;
FIG. 2 is a network reconfiguration diagram of an IEEE33 node power distribution system;
fig. 3 is a voltage comparison graph.
Detailed Description
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
The invention relates to a method for configuring the position and constant volume of a distributed power supply accessed to a power distribution network, which comprises the following steps:
(1) establishing configuration mathematical model according to distributed power supply access distribution network structure
Comprehensively considering the structure of the distributed power supply accessed to the power distribution network, establishing an optimization model of the distributed power supply accessed to the power distribution network by combining network reconstruction (technology for improving reliability, reducing line loss, balancing load and improving power supply voltage quality by changing the topological structure of the power distribution network) with the aim of reducing the active network loss of the power distribution network, wherein the objective function expression of the optimization model is as follows
Figure BDA0002373522260000041
Wherein i represents the ith branch of the power distribution network, L is the total number of the branches of the power distribution network, and riIs a branch resistance; piAnd QiRespectively the active power and the reactive power of the flowing branch; k is a radical ofiRepresenting the state of the switch on the branch, 0 when open, 1, V when closediIs the branch end voltage.
(2) Establishing constraints for an objective function
(21) Node voltage constraint
Uimin≤Ui≤Uimax(2)
Where Ui denotes the voltage of the ith load node, UiminAnd UimaxRepresenting the upper and lower voltage limits of the load node, respectively.
(22) Line current confinement
Ij＜Ijmax(3)
Wherein IjRepresenting the current of the j-th line, IjmaxRepresenting the maximum current allowed on the jth line.
(23) And (3) power flow constraint: i.e. the power flow equation must be satisfied
Figure BDA0002373522260000042
Wherein P isisFor active injection power of node i, QisReactive injection power, U, for node iiIs the voltage amplitude of node i, UjIs the voltage amplitude of node j, GijFor the conductance of the branch between nodes i and j, BijSusceptance, 0, for the branch between nodes i and jijRepresenting the difference in the voltage phase at node i and node j.
(24) Branch capacity constraint
Sj≤Sjmax(5)
Wherein SjTo branch capacity, SjmaxThe maximum capacity allowed for transmission for the leg.
(25) DG output constraint
PDG，imin≤PDG，i≤PDG，imax(6)
Wherein P isDG，iDenotes the capacity, P, of the ith distributed power supplyDG，iminIs the minimum value of the ith distributed power source capacity, PDG，imaxIs the maximum value of the ith distributed power source capacity.
(3) Optimal solution for solving objective function by particle swarm optimization
The flow is shown in figure 1. And (3) configuring the position and the capacity of the distributed power supply as variables, taking a formula (1) as an objective function, and solving an optimal solution by adopting a particle swarm algorithm.
(31) And (5) initializing. And setting parameters of a particle swarm algorithm, including the swarm scale, the maximum iteration times and the learning factor of the particle swarm, reading original data of the power distribution network, and obtaining branch information of the related nodes.
(32) Randomly generating an initial particle population, and randomly generating M particles, wherein M is random generation, the number of the initial particles in the test is 30, and each particle contains two pieces of basic information: position and velocity. The positions in the particles represent possible solutions in formula (1), according to 30 solutions generated randomly, the 30 solutions are brought into formula (1) to obtain the minimum value and the local optimal solution in the 30 solutions, meanwhile, the positions and the speeds of the 30 particles are updated according to formula (7), and in each iteration of the particle swarm optimization, the position and the speed of each particle are updated.
Figure BDA0002373522260000051
Wherein
Figure BDA0002373522260000052
The method is characterized in that the method represents the component of the t +1 th flight velocity vector of the iteration of the particle i in the d dimension, omega represents an inertia factor, the capability of locally searching the optimal solution is strong when omega is large, and the reverse is true. OmegamaxAnd ωminMaximum and minimum ω values, respectively. C1And C2Is the learning factor of the learning factor,
Figure BDA0002373522260000055
represents the optimal solution that the particle i has experienced,
Figure BDA0002373522260000053
representing the optimal solution experienced by the population.
Figure BDA0002373522260000054
Representing the d-dimensional component, T, of the flight position vector of the T +1 iterative particle imaxRepresenting the maximum number of iterations and t representing the number of current iterations.
(33) Carrying out load flow calculation on a power distribution network corresponding to each particle, calculating the particle fitness to obtain an individual optimal solution of the particles, taking the particles with the maximum fitness as a global optimal solution, and setting the current iteration time t to be 1;
(34) judging whether the maximum iteration number T is reachedmaxIf so, outputting the optimal solution of the current group, namely the optimal solution of the objective function (1), and ending;
(35) updating the speed and the position of the particles according to an updating formula, updating the individual extreme value and the global optimal solution, and judging;
(36) and (5) when the iteration time t is t +1, continuing to calculate and solve in the fourth step.
The selected cases are calculated and analyzed by the method: the IEEE33 nodes are used as cases for analysis, 32 branches are shared in the IEEE33 nodes, voltage values (according to a load flow calculation formula of a power distribution network) and network loss conditions of all the nodes are calculated by selecting different branch positions and different DG capacities in a given distribution network structure, and the best access position is selected by comparing the voltage values with initial voltage values of an IEEE33 distribution network, so that the optimal solution is obtained.
In order to verify that the optimal position for accessing the distributed power supply can be selected by adopting the research method for the position and the constant volume of the distributed power supply access distribution network provided by the invention, the inventor adopts a particle swarm algorithm to solve and screen out the position and the capacity which are most suitable for accessing the distributed power supply. The particle swarm algorithm parameters are set as follows: population size 30, maximum iteration number 50, learning factor C1＝C22.0, inertial weight ωmax＝0.9，ωminThe reconstructed IEEE33 node power distribution system is shown in fig. 2 below, which is 0.4.
It can be seen from the power distribution network structure of fig. 2 that the loss of the power distribution network with the distributed power supply is reduced as shown in table 1, and the voltage quality of the power distribution network is improved as shown in fig. 3. The feasibility and the accuracy of the method provided by the invention are proved.
Table 1 IEEE33 node power distribution system reconstruction results
Figure BDA0002373522260000061

Claims (5)

1. A configuration method for the position and constant volume of a distributed power supply connected to a power distribution network is characterized by comprising the following steps:
(1) accessing a distribution network structure according to a distributed power supply, calculating the loss of the distribution network through the active power and the reactive power of branches of the structure, and establishing a configuration mathematical model; the configuration mathematical model is as follows:
Figure FDA0002373522250000011
wherein i represents the ith branch of the power distribution network, L is the total number of the branches of the power distribution network, and riIs a branch resistance; piAnd QiRespectively the active power and the reactive power of the flowing branch; k is a radical ofiRepresenting the state of the switch on the branch, 0 when open, 1, V when closediIs the branch end voltage;
(2) establishing constraint conditions for the configuration mathematical model;
(3) and solving the optimal solution of the configuration mathematical model by adopting a particle swarm algorithm to obtain the optimal distributed power supply position and capacity configuration.
2. The method according to claim 1, wherein the constraint conditions in step (2) include node voltage constraint, line current constraint, power flow constraint, branch capacity constraint, and DG output constraint.
3. The method for configuring the position and the constant volume of the distributed power supply accessed to the power distribution network according to claim 2, wherein the constraint conditions in the step (2) are as follows:
(21) node voltage constraint
Uimin≤Ui≤Uimax
Where Ui denotes the voltage of the ith load node, UiminAnd UimaxRespectively representing the upper limit and the lower limit of the voltage of the load node;
(22) line current confinement
Ij＜Ijmax
Wherein IjRepresenting the current of the j-th line, IjmaxRepresents the maximum current allowed on the jth line;
(23) and (3) power flow constraint: i.e. the power flow equation must be satisfied
Figure FDA0002373522250000012
Wherein P isisFor active injection power of node i, QisReactive injection power, U, for node iiIs the voltage amplitude of node i, UjIs the voltage amplitude of node j, GijFor the conductance of the branch between nodes i and j, BijFor branch susceptance, θ, between nodes i and jijRepresenting the difference in the voltage phase at node i and node j.
(24) Branch capacity constraint
Sj≤Sjmax
Wherein SjTo branch capacity, SjmaxMaximum capacity allowed for the tributary for transmission;
(25) DG output constraint
PDG，imin≤PDG，i≤PDG，imax
Wherein P isDG，iDenotes the capacity, P, of the ith distributed power supplyDG，iminIs the minimum value of the ith distributed power source capacity, PDG，imaxIs the maximum value of the ith distributed power source capacity.
4. The method for configuring the position and the constant volume of the distributed power supply accessed to the power distribution network according to claim 1, wherein the step (3) of solving by using the particle swarm algorithm and configuring the optimal solution of the mathematical model comprises the following steps:
(31) setting parameters of a particle swarm algorithm, including the swarm scale, the maximum iteration times and the learning factors of the particle swarm, reading original data of the power distribution network, and obtaining branch information of related nodes;
(32) randomly generating an initial particle population, randomly generating M particles to form the initial population and endowing the initial population with an initial speed;
(33) carrying out load flow calculation on a power distribution network corresponding to each particle, calculating the particle fitness to obtain an individual optimal solution of the particles, taking the particles with the maximum fitness as a global optimal solution, and setting the current iteration time t to be 1;
(34) judging whether the maximum iteration number T is reachedmaxIf so, outputting the optimal solution of the current group, and ending;
(35) updating the speed and the position of the particles according to an updating formula, updating the individual extreme value and the global optimal solution, and judging;
(36) and (5) continuing to calculate and solve in the step (34) when the iteration time t is t + 1.
5. The method for configuring the location and volume of the distributed power supply connected to the power distribution network according to claim 4, wherein the updating formula in the step (35) is as follows:
Figure FDA0002373522250000021
wherein v isidRepresenting the component of the particle i's flying velocity vector in the d-dimension, ω representing the inertia factor, ωmaxAnd ωminMaximum and minimum ω values, respectively; c1And C2Is the learning factor of the learning factor,
Figure FDA0002373522250000022
represents the optimal solution that the particle i has experienced,
Figure FDA0002373522250000023
representing the optimal solution of the population; x is the number ofidRepresenting the flight position vector of the particle i in the d-component, TmaxRepresenting the maximum number of iterations and t representing the number of current iterations.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014114107A1 (en) * 2013-01-22 2014-07-31 国家电网公司 Method for optimizing optimal power flow of distributed generations
CN108832615A (en) * 2018-05-08 2018-11-16 中国电力科学研究院有限公司 A kind of reconstruction method of power distribution network and system based on improvement binary particle swarm algorithm
CN109217284A (en) * 2017-07-05 2019-01-15 南京理工大学 A kind of reconstruction method of power distribution network based on immune binary particle swarm algorithm
CN110247390A (en) * 2019-01-30 2019-09-17 国网浙江安吉县供电有限公司 A kind of polymorphic type distributed generation resource Optimal Configuration Method based on immunity particle cluster algorithm

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2014114107A1 (en) * 2013-01-22 2014-07-31 国家电网公司 Method for optimizing optimal power flow of distributed generations
CN109217284A (en) * 2017-07-05 2019-01-15 南京理工大学 A kind of reconstruction method of power distribution network based on immune binary particle swarm algorithm
CN108832615A (en) * 2018-05-08 2018-11-16 中国电力科学研究院有限公司 A kind of reconstruction method of power distribution network and system based on improvement binary particle swarm algorithm
CN110247390A (en) * 2019-01-30 2019-09-17 国网浙江安吉县供电有限公司 A kind of polymorphic type distributed generation resource Optimal Configuration Method based on immunity particle cluster algorithm

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