CN114912235B - Network loss collaborative optimization method for micro-grid community - Google Patents

Network loss collaborative optimization method for micro-grid community Download PDF

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CN114912235B
CN114912235B CN202210594318.1A CN202210594318A CN114912235B CN 114912235 B CN114912235 B CN 114912235B CN 202210594318 A CN202210594318 A CN 202210594318A CN 114912235 B CN114912235 B CN 114912235B
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CN114912235A (en
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陈昌松
徐志文
张竞月
杨天昊
段善旭
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Huazhong University of Science and Technology
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Abstract

The invention discloses a network loss collaborative optimization method of a micro-grid community, which belongs to the technical field of grid optimization and comprises the following steps: establishing an access optimization model and an optimization reconstruction model of a micro-grid community; the method comprises the steps of accessing an optimization model, wherein decision variables are node positions of access to a power distribution network of each micro-grid in a micro-grid community, an objective function is newly increased network loss of the micro-grid community, and constraint conditions are power flow constraint of the power distribution network; the decision variable of the optimal reconstruction model is a branch switch state combination of each micro grid in the micro grid community, the objective function of the optimal reconstruction model is the basic network loss of the micro grid community, and the constraint condition of the optimal reconstruction model is the tide constraint and the network topology constraint of the micro grid; and performing collaborative optimization on the access optimization model and the optimization reconstruction model to obtain node positions of access of all the micro-grids to the power distribution network in the micro-grid community and branch switch state combinations of all the micro-grids when the total network loss of the micro-grid community is minimized. The invention can effectively reduce the network loss of the micro-grid community.

Description

Network loss collaborative optimization method for micro-grid community
Technical Field
The invention belongs to the technical field of power grid optimization, and particularly relates to a network loss collaborative optimization method for a micro-grid community.
Background
In recent years, with the development of the national "two carbon" goal, renewable energy sources and electric vehicles are gradually replacing fossil energy sources and traditional fuel vehicles. Renewable energy sources have extremely large fluctuation and intermittence, and after being integrated into a power distribution system in a large scale, the renewable energy sources can lead to slow system adjustment and unstable operation. The electric automobile has higher randomness and can influence the safe operation of the power distribution system. In order to efficiently consume and utilize distributed renewable energy sources, electric vehicles are orderly served, and micro-grids (MG) are generated. The micro-grid is a small power generation and distribution integrated system composed of a distributed power supply, an energy storage unit, an energy conversion device and the like, is a unit power supply system in a power distribution network, and has the characteristics of small scale, rapid control and adjustment and high flexibility. However, the random access operation of the micro-grid can change the network structure of the power distribution network, so that the network loss of the system is increased, the environmental protection of the system is reduced, and therefore, the node of the micro-grid accessed to the power distribution network needs to be controlled.
Network reconstruction is one of the important means of optimizing the traditional power distribution network, and the topology structure of the power distribution network is modified by changing the states of the tie switches and the sectionalizing switches, so that the aims of reducing network loss, improving voltage quality and the like are achieved. The technical means can be also applied to the micro-grid, the network loss of the micro-grid is reduced through optimizing and reconstructing, and the environmental protection of the micro-grid is improved. At present, few researches are focused on the micro-grid optimization reconstruction technology, the micro-grid optimization reconstruction technology is usually used for reconstructing a single micro-grid containing renewable energy sources, the micro-grid optimization reconstruction technology is rarely interacted with a power distribution network, the micro-grid community integrating large-scale electric vehicles and renewable energy sources is hardly researched, and meanwhile, the influence of access to power distribution network nodes is considered. In addition, most of existing optimal reconstruction aiming at the micro-grid is fault reconstruction, namely, when a micro-grid branch breaks down, the topology structure in the micro-grid is reconstructed so as to realize load balance.
In general, the existing method only considers the reconstruction of a single micro-grid containing renewable energy sources, does not deeply study the influence of the cooperative interaction of an electric vehicle, the renewable energy sources, a micro-grid community and a power distribution network on the network loss, has a simpler applicable scene, can not effectively reduce the network loss of the micro-grid community in practical application, and improves the network operation condition.
Disclosure of Invention
Aiming at the defects and improvement demands of the prior art, the invention provides a network loss collaborative optimization method of a micro-grid community, and aims to effectively reduce the network loss of the micro-grid community.
To achieve the above object, according to one aspect of the present invention, there is provided a network loss collaborative optimization method for a micro-grid community, including:
Establishing an access optimization model and an optimization reconstruction model of a micro-grid community; the method comprises the steps of accessing an optimization model, wherein decision variables are node positions of access to a power distribution network of each micro-grid in a micro-grid community, an objective function is newly increased network loss of the micro-grid community, and constraint conditions are power flow constraint of the power distribution network; the decision variable of the optimal reconstruction model is a branch switch state combination of each micro grid in the micro grid community, the objective function of the optimal reconstruction model is the basic network loss of the micro grid community, and the constraint condition of the optimal reconstruction model is the tide constraint and the network topology constraint of the micro grid;
and performing collaborative optimization on the access optimization model and the optimization reconstruction model to obtain node positions of access of all the micro-grids to the power distribution network in the micro-grid community and branch switch state combinations of all the micro-grids when the total network loss of the micro-grid community is minimized.
Further, performing collaborative optimization on the access optimization model and the optimization reconstruction model, including:
(S0) taking one model of the access optimization model and the optimization reconstruction model as an outer ring model and the other model as an inner ring model; taking decision variables of the outer ring model as outer ring particles, taking an objective function of the outer ring model as an adaptability function of the outer ring particles, and taking constraint conditions of the outer ring model as outer ring constraint conditions; taking decision variables of the inner ring model as inner ring particles, taking an objective function of the inner ring model as an adaptability function of the inner ring particles, and taking constraint conditions of the inner ring model as inner ring constraint conditions;
(S1) generating and initializing a population of outer ring particles;
(S2) for each outer ring particle in the outer ring particle swarm, carrying out cyclic iteration on the inner ring model by utilizing a particle swarm optimization algorithm under the condition that the variable corresponding to the outer ring particle is fixed, so as to obtain the globally optimal inner ring particle corresponding to each outer ring particle;
(S3) calculating the fitness function value of each outer ring particle according to the global optimal inner ring particle corresponding to each outer ring particle, and optimizing according to the fitness function value to obtain the local optimal outer ring particle and the global optimal outer ring particle;
(S4) updating the speed and position of the outer ring particles in the case that the outer ring constraint condition is satisfied, and performing steps (S2) to (S3) to update the locally optimal outer ring particles and the globally optimal outer ring particles in the outer ring particle swarm;
And (S5) repeatedly executing the step (S4) until the maximum outer ring iteration times are reached, and outputting globally optimal outer ring particles and corresponding globally optimal inner ring particles.
Further, in the step (S2), the cyclic iteration is performed on the inner ring model by using a particle swarm optimization algorithm, including:
(T1) generating and initializing a population of inner ring particles;
(T2) calculating fitness function values of each inner ring particle in the inner ring particle swarm, and optimizing according to the fitness function values to obtain local optimal inner ring particles and global optimal inner ring particles;
(T3) updating the speed and position of the inner ring particles in case the inner ring constraint condition is satisfied, and performing step (T1) to update the locally optimal inner ring particles and the globally optimal inner ring particles in the inner ring particle group;
And (T4) repeatedly executing the step (T3) until the maximum inner ring iteration number is reached, and outputting globally optimal inner ring particles.
Further, the outer loop model is an access optimization model, and the inner loop model is an optimization reconstruction model.
Further, the newly added loss of the micro-grid community is as follows: M represents the number of microgrids in the microgrid community; c dnws,m represents newly increased network loss generated by the power distribution network when the m-th micro power grid is connected into the power distribution network, and the calculation formula is as follows:
The network loss of the power distribution network is the network loss of the power distribution network under the condition that a micro-grid community is connected to operate; the method is characterized by comprising the following steps of obtaining basic network loss of a power distribution network under the condition that a micro-grid community is not connected to operate; After the micro-grid is connected into the power distribution network to operate, newly increased network loss of the power distribution network is caused; n dn is the number of branches of the power distribution network, i and j represent nodes in the power distribution network, and ij represents branches between the nodes i and j; k i is the switch state of branch ij; r ij is the resistance of branch ij; And Injecting initial active power and reactive power of the branch ij at t time intervals respectively; v ij,t denotes the voltage of the branch ij in the t period; k m represents a connection state of the mth micro-grid and the branch ij, k m =1 represents that the mth micro-grid is connected to the branch ij, and k i =0 represents that the mth micro-grid is not connected to the branch ij; t represents a scheduling period; And Respectively representing the active exchange power and the reactive exchange power of the mth micro-grid in the t period and the power distribution network,The calculation formula of (2) is as follows:
And The load requirements of the m micro-grids in the period t, the active power generated by the photovoltaic module, the active power generated by the wind driven generator and the active power charged by the electric automobile are respectively shown.
Further, the power flow constraint of the power distribution network includes:
Power constraint of nodes in a power distribution network:
voltage constraint of nodes in a power distribution network:
Vi min≤Vi,t≤Vi max
Power constraint of branches in a power distribution network:
current constraint of branches in a power distribution network:
Wherein P li,t and Q li,t are respectively an active load and a reactive load of a node I in a t period in the power distribution network, V i,t and V j,t are respectively voltages of the node I and j in the t period, G ij、Bij and delta ij are respectively conductance, susceptance and phase angle difference of a branch ij in the power distribution network, P ij,t and Q ij,t are respectively an active power and a reactive power of the branch ij in the t period, and I ij is a current of the branch ij; v i min and V i max represent the lower and upper voltage limits of node i, respectively, P ij,max and Q ij,max represent the upper active and reactive power limits of branch ij, Indicating the upper current limit of branch ij.
Further, the basic network loss of the micro-grid community is as follows: M represents the number of microgrids in the microgrid community; c mgws,m represents the network loss of the mth micro-grid due to the connection of the electric automobile and the renewable energy power generation device, and the calculation formula is as follows:
The basic network loss of a single micro-grid; n mg is the number of branches in the m-th micro-grid, a and b are nodes in the m-th micro-grid, ab is a branch between the nodes a and b, k a is the switch state of the branch ab, R ab is the resistance of the branch ab, AndThe initial active power and reactive power of branch ab are injected for the t period respectively,AndRespectively generating active power and reactive power by a renewable energy power generation device in an mth micro-grid in a t period; And Respectively representing the power rate and reactive power generated by charging the electric vehicle in the mth micro-grid in the t period, wherein k e represents the connection state of the electric vehicle in the t period and the branch ab in the mth micro-grid, k e =1 represents that the branch ab is connected with the electric vehicle, and k e =0 represents that no electric vehicle is connected with the branch ab; k r represents a connection state of the renewable energy power generation device and a branch ab in the m-th micro-grid, k r =1 represents that the branch ab is connected with the renewable energy power generation device, and k r =0 represents that no renewable energy power generation device is connected with the branch ab; t denotes a scheduling period.
Further, for any mth microgrid, the flow constraints of the microgrid include:
node power constraint in micro-grid:
node voltage constraints in micro-grids:
branch power constraints in micro-grids:
Branch current constraints in micro-grids:
wherein, AndThe initial active power and the reactive power of a node a in the micro-grid at the t period are respectively, P la,t and Q la,t are respectively the active load and the reactive load of the node a at the t period, V a,t and V b,t are respectively the voltages of the node a and the node b at the t period, and G ab、Bab and delta ab are respectively the electric conductance, the susceptance and the phase angle difference of a branch ab in the micro-grid; p ab,t and Q ab,t are respectively the active power and the reactive power of the branch ab in the t period, and I ab is the current of the branch ab; And Respectively representing the lower and upper voltage limits of node a, P ab,max and Q ab,max respectively representing the upper active and reactive power limits of branch ab,Indicating the upper current limit of branch ab.
Further, for any mth microgrid, the network topology constraints include:
KMG∈DMG
Wherein K MG is the switch state combination of each branch in the micro-grid; d MG is a set of branch switch state combinations that keep the microgrid operating properly.
According to another aspect of the present invention, there is provided a computer readable storage medium comprising a stored computer program; when the computer program is executed by the processor, the equipment where the computer readable storage medium is located is controlled to execute the network loss collaborative optimization method of the micro-grid community.
In general, through the above technical solutions conceived by the present invention, the following beneficial effects can be obtained:
(1) The micro-grid group consisting of the micro-grids containing renewable energy sources and electric vehicles falls in the process of interacting with the power distribution network, and the generated network loss comprises two parts, namely a newly-added network loss and a basic network loss; the newly increased network loss is generated by accessing the micro-grid into the power distribution network, and the basic network loss is generated by accessing the electric automobile and the renewable energy power generation device into the micro-grid. The invention respectively establishes the access optimization model and the optimization reconstruction model, wherein the access optimization model optimizes the node position of each micro-grid in the micro-grid community to the power distribution network to minimize the newly increased network loss, and optimizes the topological structure of each micro-grid in the micro-grid community to minimize the basic network loss, and finally optimizes the cooperation of the two models to minimize the total network loss of the micro-grid community.
(2) The method for carrying out cooperative optimization on the access optimization model and the optimization reconstruction model takes the two models as an inner ring and a double ring respectively, adopts a particle swarm optimization algorithm to realize the cooperative optimization of the double ring, can accurately and rapidly realize the cooperative optimization of the two models, and obtains the total network loss of the optimal micro-grid community; in the preferred scheme, the access optimization model is used as an outer ring, and the optimization reconstruction model is used as an inner ring model, so that the computational complexity can be further reduced.
(3) The invention is suitable for a micro-grid community comprising a plurality of grid-connected micro-grids, wherein the micro-grids in the community comprise renewable energy power generation devices and electric automobile charging devices, and are connected into a power distribution network to exchange power with the power distribution network.
(4) The method can be used for guiding the node position of the grid-connected micro-grid in the micro-grid community to be connected to the power distribution network, so that the newly-increased network loss of the power distribution network after the micro-grid community is connected to operate is reduced, and the newly-increased network loss of the micro-grid community is reduced; the invention can also be used for guiding the network structure reconstruction of the micro-grid in the micro-grid community, and realizing the lowest basic network loss of the micro-grid by obtaining the optimal switch action combination of the micro-grid network, thereby reducing the total network loss of the micro-grid community.
Drawings
Fig. 1 is a network structure diagram of a single micro-grid in a micro-grid community provided by an embodiment of the present invention;
fig. 2 is a schematic diagram of a network structure of a power distribution network according to an embodiment of the present invention;
Fig. 3 is a schematic diagram of a network loss collaborative optimization method of a micro-grid community according to an embodiment of the present invention;
Fig. 4 is a flowchart of dual-loop collaborative optimization provided in an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention. In addition, the technical features of the embodiments of the present invention described below may be combined with each other as long as they do not collide with each other.
In the present invention, the terms "first," "second," and the like in the description and in the drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order.
In order to solve the technical problem that the network loss of a micro-grid community cannot be effectively reduced by the existing micro-grid optimization reconstruction method, the invention provides a network loss collaborative optimization method of the micro-grid community, which has the following overall thought: considering the influence of the cooperative interaction of the electric vehicle, the renewable energy source, the micro-grid community and the power distribution network on the network loss of the micro-grid community, specifically, the micro-grid community formed by the micro-grid containing the renewable energy source and the electric vehicle falls in the process of interacting with the power distribution network, wherein the generated network loss comprises two parts, namely newly increased network loss and basic network loss; the newly increased network loss is generated by accessing a micro-grid into a power distribution network for operation, and the basic network loss is generated by accessing an electric automobile and a renewable energy power generation device into the micro-grid; corresponding optimization models are respectively established for the newly increased network loss and the basic network loss, and the two models are subjected to cooperative optimization, so that the total network loss of a micro-grid community is effectively reduced, and the running condition of the micro-grid is improved.
Before explaining the technical scheme of the invention in detail, basic concepts related to electric vehicles, renewable energy sources, micro-grid communities and distribution networks and interaction relations of the four are briefly described as follows.
A microgrid community comprising a plurality of grid-connected direct current microgrids; fig. 1 shows a topology of a single micro-grid, which is an IEEE-9 node structure, and can be reconstructed. Each micro-grid is connected with a network node of the power distribution network and exchanges energy with the power distribution network. Fig. 2 shows a topology of a power distribution network, an IEEE-33 node structure, which may also be reconfigured. The micro-grid contains a renewable energy power generation device and an electric automobile, and the situation that different types of micro-grids are connected with the renewable energy power generation device and the electric automobile can be different. In the following description, if there is no special explanation, the subscript "m" in the symbol indicates that the symbol is the parameter related to the mth micro-grid in the micro-grid community, and the subscript "t" in the symbol indicates that the symbol is the parameter related to the t period.
The renewable energy power generation is calculated by adopting a physical formula, and the wind power generation model is as follows:
Is the active power of the wind driven generator in the m-th micro-grid in the t period, For the reactive power to be supplied,For the power factor angle of the wind driven generator, P r is the rated output power of the wind driven generator, v i is the cut-in wind speed, v r is the rated wind speed, v o is the cut-out wind speed, and v t is the measured wind speed in the t period.
The photovoltaic power generation model is as follows:
m represents the number of the micro-grid, Is the generated power of the photovoltaic module in the m-th micro-grid in the period t,For the reactive power to be supplied,E t is the illumination intensity of the period t, A is the photovoltaic panel area, and eta pv is the photovoltaic panel conversion efficiency.
For the electric automobile, the random behavior of an automobile owner is considered, and the charging power of the electric automobile in the micro-grid community is obtained by random distribution of Monte Carlo. When the electric automobile is connected into the micro-grid community, the charging model of the electric automobile in the micro-grid is as follows:
wherein, The charging power of all electric vehicles in the mth micro-grid in the period t,Is the mathematical expectation of the charging power of all electric vehicles in the mth micro-grid; the random parameters generated by the t-period Monte Carlo method are normally distributed; Is the standard deviation of the charging power of the electric automobile.
The total reactive power generated by charging the electric automobile in the m-th micro-grid is as follows:
Is a power factor angle of a known electric vehicle charging device.
Based on the obtained renewable energy output and the electric vehicle data, the exchange power of the micro-grid community and the power distribution network can be calculated by combining the obtained load demand as follows:
for the active exchange power of the mth micro-grid in the t period and the power distribution network, when And when the power consumption is larger than 0, the micro-grid absorbs active power to the power distribution network, otherwise, the micro-grid injects the active power to the power distribution network.For the reactive power exchange of the mth micro-grid and the distribution network in the t period,To know the power factor angle of the microgrid to distribution network connection,Load demand for the mth microgrid for period t.
The total loss C wsM of the micro-grid community is the sum of the newly added loss C dnwsM of the micro-grid community and the basic loss C mgwsM of the micro-grid community, C dnwsM is obtained by summing the newly added loss C dnws,m of each individual micro-grid in the micro-grid community, C mgwsM is obtained by summing the basic loss C mgws,m of each individual micro-grid in the micro-grid community, and the total loss C wsM can be expressed as:
c dnws,m is a newly increased network loss generated by the power distribution network due to the fact that independent micro-grids in the micro-grid community are connected into the power distribution network. The network loss is generated by the access operation of the micro-grid and is called as a newly added network loss of the micro-grid, and the calculation formula is as follows:
The network loss of the power distribution network is the network loss of the power distribution network under the condition that a micro-grid community is connected to operate; the method is characterized by comprising the following steps of obtaining basic network loss of a power distribution network under the condition that a micro-grid community is not connected to operate; After the micro-grid is connected into the power distribution network to operate, newly increased network loss of the power distribution network is caused; n dn is the number of branches of the power distribution network, i and j represent nodes in the power distribution network, and ij represents branches between the nodes i and j; k i is the switch state of branch ij; r ij is the resistance of branch ij; And Injecting initial active power and reactive power of the branch ij at t time intervals respectively; v ij,t denotes the voltage of the branch ij in the t period; k m represents a connection state of the mth micro-grid and the branch ij, k m =1 represents that the mth micro-grid is connected with the branch ij, k i =0 represents that the mth micro-grid is not connected with the branch ij, and when the micro-grid is connected with any node in the branch, the micro-grid is considered to be connected with the branch; t represents a scheduling period;
According to According to the calculation formula of the network loss control method, the influence on the newly increased network loss when the micro-grid is connected into the power distribution network and interacted with the power distribution network is fully considered; And Reference is made to the above equation (6), according toAndAccording to the calculation formula of the system, the influence of renewable energy sources and interaction between the electric vehicle and the micro-grid on the newly increased grid loss is fully considered.
C mgws,m is a network loss generated by the electric automobile and the renewable power generation device aiming at a single micro-grid in the micro-grid community, and the micro-grid generates the network loss, so that the generated network loss is called as a basic network loss of the micro-grid. The calculation formula is as follows:
The basic network loss of a single micro-grid; n mg is the number of branches in the m-th micro-grid, a and b are nodes in the m-th micro-grid, ab is a branch between the nodes a and b, k a is the switch state of the branch ab, R ab is the resistance of the branch ab, AndThe initial active power and reactive power of branch ab are injected for the t period respectively,AndRespectively generating active power and reactive power by a renewable energy power generation device in an mth micro-grid in a t period; And Respectively representing the power rate and reactive power generated by charging the electric vehicle in the mth micro-grid in the t period, wherein k e represents the connection state of the electric vehicle in the t period and the branch ab in the mth micro-grid, k e =1 represents that the branch ab is connected with the electric vehicle, and k e =0 represents that no electric vehicle is connected with the branch ab; k r represents a connection state of the renewable energy power generation device and a branch ab in the m-th micro-grid, k r =1 represents that the branch ab is connected with the renewable energy power generation device, and k r =0 represents that no renewable energy power generation device is connected with the branch ab;
And The calculated expression of (2) is:
Wherein k 1 =1 indicates that the mth micro-grid of the t period is connected with the photovoltaic module, and k 1 =0 indicates that the mth micro-grid of the t period is not connected with the photovoltaic module; k 2 =1 indicates that the mth micro-grid of the t period is connected to the wind power generator, and k 2 =0 indicates that the mth micro-grid of the t period is not connected to the wind power generator.
In the invention, the influence of the electric automobile and the renewable energy source access micro-grid on the network loss is fully considered in the calculation of the basic network loss.
The following are examples.
Example 1:
A network loss collaborative optimization method of a micro-grid community is shown in fig. 3, and comprises the following steps:
Establishing an access optimization model and an optimization reconstruction model of a micro-grid community; the method comprises the steps of accessing an optimization model, wherein decision variables are node positions of access to a power distribution network of each micro-grid in a micro-grid community, an objective function is newly increased network loss of the micro-grid community, and constraint conditions are power flow constraint of the power distribution network; the decision variable of the optimal reconstruction model is a branch switch state combination of each micro grid in the micro grid community, the objective function of the optimal reconstruction model is the basic network loss of the micro grid community, and the constraint condition of the optimal reconstruction model is the tide constraint and the network topology constraint of the micro grid;
and performing collaborative optimization on the access optimization model and the optimization reconstruction model to obtain node positions of access of all the micro-grids to the power distribution network in the micro-grid community and branch switch state combinations of all the micro-grids when the total network loss of the micro-grid community is minimized.
In this embodiment, the objective function of the access optimization model, that is, the newly added network loss of the micro-grid community, may be calculated according to the above formula (8); the constraint conditions of the access optimization model, namely the power flow constraint of the power distribution network specifically comprises the power constraint of a node in the power distribution network, the voltage constraint of the node, the power constraint of a branch and the current constraint of the branch, and the calculation formulas are respectively shown as the following formulas (11) to (14):
Vi min≤Vi,t≤Vi max (12)
P li,t and Q li,t are respectively an active load and a reactive load of a node I in a t period in a power distribution network, V i,t and V j,t are respectively voltages of the node I and j in the t period, G ij、Bij and delta ij are respectively conductance, susceptance and phase angle difference of a branch ij in the power distribution network, P ij,t and Q ij,t are respectively an active power and a reactive power of the branch ij in the t period, and I ij is a current of the branch ij; v i min and V i max represent the lower and upper voltage limits of node i, respectively, P ij,max and Q ij,max represent the upper active and reactive power limits of branch ij, Indicating the upper current limit of branch ij.
In this embodiment, the objective function of the optimal reconstruction model, that is, the basic network loss of the micro-grid community, may be calculated according to the above formula (9); in the constraint conditions of the optimization reconstruction model, the flow constraint of the micro-grid specifically comprises node power constraint, node voltage constraint, branch power constraint and branch current constraint in the power distribution network, and the calculation formulas are respectively shown as the following formulas (15) to (18):
In the constraint condition of the optimization reconstruction model, the calculation formula of the network topology constraint is as follows:
KMG∈DMG (19)
K MG is a switch state combination of each branch in the micro-grid; d MG is a set of branch switch state combinations that keep the micro-grid operating normally; when the reconstruction of the micro-grid is involved, the network switch combination after the reconstruction of the micro-grid must be assigned to the set D MG.
As an optional implementation manner, when the access optimization model and the optimization reconstruction model are cooperatively optimized, the access optimization model and the optimization reconstruction model are respectively used as an outer ring and an inner ring, correspondingly, the node position of each micro grid in the micro grid community, where each micro grid is accessed into the power distribution network, is used as an outer ring particle, the newly added grid loss of the micro grid community is used as an adaptability function of the outer ring particle, and the power flow constraint of the power distribution network is used as a constraint condition of the outer ring; and combining branch switch states of all the micro-grids in the micro-grid community to serve as inner-loop particles, taking basic network loss of the micro-grid community as an adaptability function of the inner-loop particles, and taking tide constraint and network topology constraint of the micro-grids as constraint conditions of the inner-loop. After the setting is completed, the embodiment adopts a particle swarm optimization algorithm to realize double-loop collaborative optimization, a plurality of outer loop decision variables are randomly generated firstly, then a plurality of inner loop decision variables are randomly generated for a specific outer loop decision variable to carry out iterative solution, and therefore the lowest basic network loss of a micro-grid community and the corresponding inner loop decision variable under the specific outer loop decision variable are obtained; and carrying out loop iteration in a plurality of outer loop decision variables and corresponding inner loop decision variables by adopting a particle swarm intelligent algorithm, and carrying out global optimization to obtain the outer loop decision variables and the inner loop decision variables corresponding to the minimum total network loss, namely node position optimization results of access of each micro-grid to the power distribution network in the micro-grid community and network reconstruction optimization results of each micro-grid.
As shown in fig. 4, the above-mentioned process of double-loop co-optimization specifically includes the following steps:
(S1) generating and initializing a population of outer ring particles;
In the outer ring particle swarm, each particle corresponds to a node position of a micro-grid accessed to the power distribution network, and the outer ring particle L MG can be expressed as follows:
LMG＝[L1 L2 … Lm … LM] (20)
L m is the node position of the m-th micro-grid accessed to the power distribution network, and the node positions of the micro-grids accessed to the power distribution network are different, so that the newly-increased network loss of the power distribution network can be influenced, and the newly-increased network loss of a micro-grid community is influenced; the newly increased network loss of the power distribution network after the micro-grid is connected to operate can be reduced by optimizing the reasonable node positions, so that the newly increased network loss of the micro-grid community is reduced, and the total network loss of the micro-grid community is further reduced;
(S2) for each outer ring particle in the outer ring particle swarm, setting and updating system configuration according to the outer ring particle, and carrying out cyclic iteration on the inner ring model by using a particle swarm optimization algorithm under the condition that the variable corresponding to the outer ring particle is fixed to obtain globally optimal inner ring particles corresponding to each outer ring particle;
the inner ring is nested in the outer ring optimization, and under each node position corresponding to the outer ring particles, iterative optimization of the inner ring optimization reconstruction particle swarm is carried out, so that the basic network loss of the micro-grid community is further optimized; in the continuous loop iterative optimization of the inner loop and the outer loop, the total network loss of the lowest micro-grid community can be obtained cooperatively;
In the step (S2), the cyclic iteration is performed on the inner ring model by using a particle swarm optimization algorithm, including:
(T1) generating and initializing a population of inner ring particles;
In the inner ring particle swarm, each particle corresponds to the topological structure of each micro-grid, and the inner ring particles Can be expressed as:
For the branch switch state combination of each micro-grid in the micro-grid community, K m,i is the switch state of the ith branch in the mth micro-grid, and is usually represented by 0 and 1 to be opened and closed respectively;
(T2) calculating fitness function values of each inner ring particle in the inner ring particle swarm, and optimizing according to the fitness function values to obtain local optimal inner ring particles and global optimal inner ring particles;
(T3) updating the speed and position of the inner ring particles in case the inner ring constraint condition is satisfied, and performing step (T1) to update the locally optimal inner ring particles and the globally optimal inner ring particles in the inner ring particle group;
(T4) repeatedly executing the step (T3) until the iteration number N in reaches the maximum inner ring iteration number N inmax, and outputting globally optimal inner ring particles;
(S3) calculating the fitness function value of each outer ring particle according to the global optimal inner ring particle corresponding to each outer ring particle, and optimizing according to the fitness function value to obtain the local optimal outer ring particle and the global optimal outer ring particle;
(S4) updating the speed and position of the outer ring particles in the case that the outer ring constraint condition is satisfied, and performing steps (S2) to (S3) to update the locally optimal outer ring particles and the globally optimal outer ring particles in the outer ring particle swarm;
(S5) repeatedly executing the step (S4) until the iteration number N out reaches the maximum outer ring iteration number N outmax, and outputting the globally optimal outer ring particles and the corresponding globally optimal inner ring particles.
It should be noted that, in other embodiments of the present invention, the optimized access model may be used as an inner ring model, and the optimized reconstruction model may be used as an outer ring model, where the computational complexity allows, and the solving step may refer to the above steps after the particles, fitness functions and constraints of the inner and outer rings are set accordingly.
In general, the access optimization model and the optimization reconstruction model of the micro-grid community are respectively established, and the two models are subjected to collaborative optimization in a double-loop collaborative optimization mode, so that the whole micro-grid community can be optimized, the influence of the collaborative interaction of the electric vehicle, the renewable energy source, the micro-grid community and the power distribution network on the network loss is fully considered, and the network loss of the micro-grid community is effectively reduced.
Example 2:
a computer readable storage medium comprising a stored computer program; when the computer program is executed by the processor, the device where the computer readable storage medium is located is controlled to execute the network loss collaborative optimization method of the micro-grid community provided in the embodiment 1.
It will be readily appreciated by those skilled in the art that the foregoing description is merely a preferred embodiment of the invention and is not intended to limit the invention, but any modifications, equivalents, improvements or alternatives falling within the spirit and principles of the invention are intended to be included within the scope of the invention.

Claims (5)

1. The network loss collaborative optimization method for the micro-grid community is characterized by comprising the following steps of:
Establishing an access optimization model and an optimization reconstruction model of the micro-grid community; the decision variable of the access optimization model is the node position of each micro-grid in the micro-grid community to be accessed to the power distribution network, the objective function of the access optimization model is the newly-increased grid loss of the micro-grid community, and the constraint condition of the access optimization model is the power flow constraint of the power distribution network; the decision variable of the optimization reconstruction model is a branch switch state combination of each micro grid in the micro grid community, the objective function of the optimization reconstruction model is basic grid loss of the micro grid community, and the constraint condition of the optimization reconstruction model is the flow constraint and the network topology constraint of the micro grid;
Performing collaborative optimization on the access optimization model and the optimization reconstruction model to obtain node positions of access of all micro-grids to a power distribution network in the micro-grid community and branch switch state combinations of all the micro-grids when the total network loss of the micro-grid community is minimized;
the newly added network loss of the micro-grid community is as follows: M represents the number of micro-grids in the micro-grid community; c dnws,m represents newly increased network loss generated by the power distribution network when the m-th micro power grid is connected to the power distribution network, and the calculation formula is as follows:
The network loss of the power distribution network is the network loss of the power distribution network under the condition that a micro-grid community is connected to operate; the method is characterized by comprising the following steps of obtaining basic network loss of a power distribution network under the condition that a micro-grid community is not connected to operate; After the micro-grid is connected into the power distribution network to operate, newly increased network loss of the power distribution network is caused; n dn is the number of branches of the power distribution network, i and j represent nodes in the power distribution network, and ij represents branches between the nodes i and j; k i is the switch state of branch ij; r ij is the resistance of branch ij; And Injecting initial active power and reactive power of the branch ij at t time intervals respectively; v ij,t denotes the voltage of the branch ij in the t period; k m represents a connection state of the mth micro-grid and the branch ij, k m =1 represents that the mth micro-grid is connected to the branch ij, and k i =0 represents that the mth micro-grid is not connected to the branch ij; t represents a scheduling period; And Respectively representing the active exchange power and the reactive exchange power of the mth micro-grid in the t period and the power distribution network,The calculation formula of (2) is as follows:
And The load requirements of m micro-grids in the period t, the active power generated by the photovoltaic module, the active power generated by the wind driven generator and the active power charged by the electric automobile are respectively shown;
the power flow constraint of the power distribution network comprises the following steps:
Power constraint of nodes in a power distribution network:
voltage constraint of nodes in a power distribution network:
Vi min≤Vi,t≤Vi max
Power constraint of branches in a power distribution network:
current constraint of branches in a power distribution network:
Wherein P li,t and Q li,t are respectively an active load and a reactive load of a node I in a t period in the power distribution network, V i,t and V j,t are respectively voltages of the node I and j in the t period, G ij、Bij and delta ij are respectively conductance, susceptance and phase angle difference of a branch ij in the power distribution network, P ij,t and Q ij,t are respectively an active power and a reactive power of the branch ij in the t period, and I ij is a current of the branch ij; v i min and V i max represent the lower and upper voltage limits of node i, respectively, P ij,max and Q ij,max represent the upper active and reactive power limits of branch ij, Representing the upper current limit of branch ij;
The basic network loss of the micro-grid community is as follows: M represents the number of micro-grids in the micro-grid community; c mgws,m represents the network loss of the mth micro-grid due to the connection of the electric automobile and the renewable energy power generation device, and the calculation formula is as follows:
The basic network loss of a single micro-grid; n mg is the number of branches in the m-th micro-grid, a and b are nodes in the m-th micro-grid, ab is a branch between the nodes a and b, k a is the switch state of the branch ab, R ab is the resistance of the branch ab, AndThe initial active power and reactive power of branch ab are injected for the t period respectively,AndRespectively generating active power and reactive power by a renewable energy power generation device in an mth micro-grid in a t period; And Respectively representing the power rate and reactive power generated by charging the electric vehicle in the mth micro-grid in the t period, wherein k e represents the connection state of the electric vehicle in the t period and the branch ab in the mth micro-grid, k e =1 represents that the branch ab is connected with the electric vehicle, and k e =0 represents that no electric vehicle is connected with the branch ab; k r represents a connection state of the renewable energy power generation device and a branch ab in the m-th micro-grid, k r =1 represents that the branch ab is connected with the renewable energy power generation device, and k r =0 represents that no renewable energy power generation device is connected with the branch ab; t represents a scheduling period;
for any mth microgrid, the flow constraints of the microgrid include:
node power constraint in micro-grid:
node voltage constraints in micro-grids:
branch power constraints in micro-grids:
Branch current constraints in micro-grids:
wherein, AndThe initial active power and the reactive power of a node a in the micro-grid at the t period are respectively, P la,t and Q la,t are respectively the active load and the reactive load of the node a at the t period, V a,t and V b,t are respectively the voltages of the node a and the node b at the t period, and G ab、Bab and delta ab are respectively the electric conductance, the susceptance and the phase angle difference of a branch ab in the micro-grid; p ab,t and Q ab,t are respectively the active power and the reactive power of the branch ab in the t period, and I ab is the current of the branch ab; And Respectively representing the lower and upper voltage limits of node a, P ab,max and Q ab,max respectively representing the upper active and reactive power limits of branch ab,Representing the upper current limit of branch ab;
for any mth microgrid, the network topology constraints include:
KMG∈DMG
Wherein K MG is the switch state combination of each branch in the micro-grid; d MG is a set of branch switch state combinations that keep the microgrid operating properly.
2. The network loss collaborative optimization method of a micro-grid community according to claim 1, wherein collaborative optimization of the access optimization model and the optimization reconstruction model comprises:
(S0) taking one of the access optimization model and the optimization reconstruction model as an outer loop model and the other as an inner loop model; taking decision variables of the outer ring model as outer ring particles, taking an objective function of the outer ring model as an adaptability function of the outer ring particles, and taking constraint conditions of the outer ring model as outer ring constraint conditions; taking decision variables of the inner ring model as inner ring particles, taking an objective function of the inner ring model as an fitness function of the inner ring particles, and taking constraint conditions of the inner ring model as inner ring constraint conditions;
(S1) generating and initializing a population of outer ring particles;
(S2) for each outer ring particle in the outer ring particle swarm, carrying out cyclic iteration on the inner ring model by utilizing a particle swarm optimization algorithm under the condition that the variable corresponding to the outer ring particle is fixed, so as to obtain global optimal inner ring particles corresponding to each outer ring particle;
(S3) calculating the fitness function value of each outer ring particle according to the global optimal inner ring particle corresponding to each outer ring particle, and optimizing according to the fitness function value to obtain the local optimal outer ring particle and the global optimal outer ring particle;
(S4) updating the speed and position of the outer ring particles in case the outer ring constraint condition is satisfied, and performing steps (S2) to (S3) to update the locally optimal outer ring particles and the globally optimal outer ring particles in the outer ring particle swarm;
And (S5) repeatedly executing the step (S4) until the maximum outer ring iteration times are reached, and outputting globally optimal outer ring particles and corresponding globally optimal inner ring particles.
3. The method for collaborative optimization of network loss of a micro-grid community according to claim 2, wherein in the step (S2), the loop iteration is performed on the inner loop model by using a particle swarm optimization algorithm, including:
(T1) generating and initializing a population of inner ring particles;
(T2) calculating fitness function values of each inner ring particle in the inner ring particle swarm, and optimizing according to the fitness function values to obtain locally optimal inner ring particles and globally optimal inner ring particles;
(T3) updating the velocity and position of the inner ring particles if the inner ring constraint condition is satisfied, and performing step (T1) to update the locally optimal inner ring particles and the globally optimal inner ring particles in the inner ring particle swarm;
And (T4) repeatedly executing the step (T3) until the maximum inner ring iteration number is reached, and outputting globally optimal inner ring particles.
4. The network loss collaborative optimization method of a micro-grid community according to claim 2, wherein the outer loop model is the access optimization model, and the inner loop model is the optimization reconstruction model.
5. A computer readable storage medium comprising a stored computer program; when the computer program is executed by a processor, the device where the computer readable storage medium is located is controlled to execute the network loss collaborative optimization method of the micro-grid community according to any one of claims 1 to 4.
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