CN111881626B - Distribution network planning method for promoting DG (distributed generation) digestion - Google Patents

Distribution network planning method for promoting DG (distributed generation) digestion Download PDF

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CN111881626B
CN111881626B CN202010769185.8A CN202010769185A CN111881626B CN 111881626 B CN111881626 B CN 111881626B CN 202010769185 A CN202010769185 A CN 202010769185A CN 111881626 B CN111881626 B CN 111881626B
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CN111881626A (en
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何英静
王曦冉
李海疆
李天佑
章敏捷
杨翾
石国超
徐旸
李帆
沈淑仪
但扬清
乔永亮
王妤宁
徐子涵
于湉湉
张丽婧
毛安家
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State Grid Zhejiang Electric Power Co Ltd
North China Electric Power University
Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co Ltd
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North China Electric Power University
Economic and Technological Research Institute of State Grid Zhejiang Electric Power Co Ltd
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Abstract

The invention discloses a power distribution network planning method for promoting DG digestion, which comprises the following steps: establishing an upper layer model by taking minimum network loss cost and minimum reliability cost (fault loss cost) of the power distribution network as objective functions, determining an optimal network frame structure of the power distribution network, and providing a planning scheme for a lower layer; establishing a lower model by taking the maximum DG absorption capacity as a target; introducing the crossover and mutation in the genetic algorithm into the azalea search algorithm to form an improved azalea search algorithm, and solving the established upper and lower models by using the improved azalea search algorithm; and feeding back the solving result to the upper model, solving the upper model again, and finally outputting the optimal grid structure, the DG access position and the installed capacity through repeated iteration. The method realizes minimum network loss on the basis of unchanged investment total amount, and has higher application value.

Description

Distribution network planning method for promoting DG (distributed generation) digestion
Technical Field
The invention belongs to the technical field of power system power distribution network planning, and particularly relates to a power distribution network planning method for promoting DG (distributed power supply, distributed Generation) digestion based on an improved azalea search algorithm.
Background
The large-scale development of clean energy has become the basic trend of energy development of various countries, taking China as an example, in 2018, the clean energy such as natural gas, hydropower, wind power and the like accounts for up to 24.6 percent in the total consumption of energy, and the clean energy is increased by 0.8 percent compared with 2017. According to the statistics of the national energy bureau, the water power, wind power, photovoltaic and biomass power generation installed capacity of China is respectively 3.54 hundred million kilowatts, 1.93 hundred million kilowatts, 1.86 hundred million kilowatts and 1995 ten thousand kilowatts by the end of 6 months in 2019, the clean power installation occupies more than 41% of the total installed capacity, and the clean power utilization rate is further increased.
Distributed renewable energy sources are important components of clean energy sources, and are rapidly developed in recent years due to the influence of factors such as policy support and low technological development thresholds. The development mode of 'self-power-consumption and residual internet surfing' of the distributed energy DG enables a large number of distributed power sources to be connected into the power distribution network nearby, and the original attribute of the power distribution network is affected in a non-negligible way, so that the reliability and economy of the whole power distribution network are affected. Therefore, how to consider the characteristics of clean energy in power distribution network planning to obtain a power distribution network planning model and method suitable for distributed energy access is a current urgent problem to be solved.
The distribution network is used as a bridge for communication between users and a main network, and the main purpose of planning is to meet the safe, reliable and economic electricity consumption requirements of the users. Aiming at the planning problem of the power distribution network, the industry has developed intensive research and has achieved a great deal of research results. Initially, from the targets of one aspect of the power distribution network, the targets of other aspects are not considered, so that the adaptability of a method and a model is affected to a certain extent, and more researches focus on multi-target unified planning of the power distribution network. The traditional evaluation of power distribution network planning is mainly considered from factors such as reliability, economy, social environment and the like, however, in the planning problem of the distributed energy access to the power distribution network, the energy consumption capability is considered as a new index factor.
The distribution network in China is generally affiliated to a city power supply company, and the city company is generally affiliated to a power saving company. When the power grid is required to be upgraded, modified or expanded, each city company needs to report a plan and budget to the province company according to the load increase in the area under jurisdiction and the actual condition of the power grid, and the province company needs to balance among all city companies in the whole province according to the matched condition of funds and then approve project plans and construction funds. Therefore, when the distribution network is changed and expanded, the local market company does not pursue the minimum investment and the best pursue effect, but obtains the best construction effect with proper investment within the allowable range of the funds.
Therefore, it is necessary to build a planning model of the distribution network adapted to the distributed energy access, and fully consider the total control principle of the investment costs of the distribution network transformation and construction, and implement the distribution network investment transformation in the maximum range within the allowable investment range so as to obtain the maximum reliability and economy.
Disclosure of Invention
In order to solve the problem that the model solving result is inconsistent with the actual result caused by the principle that the traditional power distribution network planning takes the minimum investment as the target and neglects the total investment control, the invention provides a power distribution network planning method for promoting DG (distributed generation) digestion based on an improved azalea search algorithm, which can effectively realize the minimum network loss on the basis of the unchanged total investment and optimize the investment scheme.
In order to achieve the above purpose, the present invention adopts the following technical scheme.
A power distribution network planning method for promoting DG (distributed generation) digestion based on an improved azalea search algorithm specifically comprises the following steps:
the power distribution network planning method for promoting DG digestion is characterized by comprising the following steps of:
step 1: establishing an upper layer model by taking the minimum fault loss cost as an objective function, wherein the fault loss cost comprises the network loss cost and the reliability cost of the power distribution network, and the method comprises the following steps:
determining an optimal grid structure of the distribution network, and providing a planning scheme for a lower layer, wherein the upper layer model is as follows:
min C＝C loss +μ(C F +C E )
wherein: c is the cost of failure loss, C loss The total cost is annual net loss; c (C) F The power failure fault expense for the system; c (C) E The loss cost for the user caused by power failure; μ is the weight occupied by the reliability index in the objective function, and the expressions of various fees in the objective function are as follows:
wherein: c o The cost is unit network loss; c F Punishment cost under the unit power failure times; c E Cost is lost for a unit power outage user; τ is the maximum network loss hours; n (N) l Is the total number of lines; n (N) k A number of types selectable for the ith line; s is the scene number; omega i The occurrence probability of the ith scene; p (P) loss，i The active power loss value of the ith line; i i A current value for the i-th scene; s is S i，k A "0-1" state variable, indicating that the ith line selects the kth model investment state, 1 indicating that the model was selected for investment, 0 indicating that the model was not selected for investment; r is R i，k The total resistance value of the ith line when the kth model of the ith line is selected;
the constraint conditions of the established upper layer model comprise:
1) Total investment cost constraints
Wherein: c (C) T，i The price of the transformer newly built or reconstructed in the ith scene is calculated; c' is the total investment cost of the distribution network;
2) Investment state constraint for candidate numbers
S i，k ∈{0，1}
3) Connectivity constraints
Each load point in the power distribution network is guaranteed to be communicated with a network, power is supplied to all load points in the area, and island conditions are avoided;
4) Tie line position constraint
The line net rack is determined by the wiring form of the power distribution network;
step 2: the method for constructing the lower model by taking the maximum DG absorption capacity as a target specifically comprises the following steps:
according to the upper model established in the step 1, establishing a lower model with the maximum DG absorption capacity as a target, feeding back a calculation result of the lower model to the upper model, and making an optimal decision by the upper model; the lower model is as follows:
wherein: c (C) R To the digestion ability of DG, P DG，i DG total capacity for node i, S i A "0-1" state variable, indicating whether the node has access to a DG;
the constraint conditions of the lower model include:
1) Power distribution network tide constraint
Wherein: p (P) i An active injection power value for node i; q (Q) i The reactive power injection power value of the node i; u (U) i ，U j The voltage amplitudes of the nodes i and j are respectively; g ij ，B ij Respectively the real part and the imaginary part of the node admittance matrix; θ ij Is the phase angle difference between nodes i and j; j epsilon i is all nodes directly connected with the node i; beta i For DG availability, a rough calculation may take 1.0;power factor angle for DG;
2) Node voltage constraint of power distribution network
U i min ≤U i ≤U i max
Wherein: u (U) i The voltage amplitude of the node i; u (U) imin The lower limit of the voltage amplitude of the node i; u (U) imax The upper limit of the voltage amplitude of the node i;
3) DG force constraint
Wherein: p (P) DG，i The DG active power value of the node i; q (Q) DG，i The DG reactive power value of the node i; p (P) DG，imax The DG active power upper limit of the node i; q (Q) DG，imax The DG reactive power upper limit of the node i;
4) Tidal current direction constraint
After DG is connected into the power distribution network, the direction of feeder line power flow is not changed, and if the power distribution network adopts a downstream numbering method, the following steps are adopted:
P ij ≥0；j＞i，j∈i；
5) Access point number constraints
When the total capacity of the planned distributed energy source is fixed, a mode of accessing the power grid nearby is adopted, and the number of access points of DGs is smaller than a certain number, which is expressed as:
wherein: n (N) k Representing DG maximum access point number;
step 3: the crossover and mutation in the genetic algorithm are introduced into the azalea search algorithm to form an improved azalea search algorithm, which is specifically as follows:
1) Rhododendrons lay one egg in any bird nest at a time randomly, which is marked as a solution, and only one egg is in one bird nest;
2) The best eggs representing the current optimal solution will be retained to the next generation;
3) The number n of bird nests for the azalea to select is certain;
4) The probability of the host mother finding a heterogeneous is P a ∈{0，1}；
5) After being found, taking the difference X between the bird nest position and the global optimal solution as the basis of the cross variation, the difference of the ith bird nest at the t generationCan be calculated by the following formula:
wherein: g t The optimal position of the t generation bird nest;the position of the ith nest at the t generation; x is x max Maximum value of bird nest position variable; x is x min Is the minimum value of the bird's nest position variable.
6) L (λ) is the Levy flight random course value, which is distributed as follows:
L(λ)～μ＝t -λ ；1＜λ≤3
wherein: u is a gravity tail power law distribution function, and lambda is an exponential correlation part;
7) The new solution is generated as follows:
wherein: h is the step size and h >0;
8) The method carries out cross mutation treatment on the bird nest position, and comprises the following substeps:
s381: determining a lower limit X of the difference X min Mutation rate p m Crossover rate p c 。
S382: judging X of ith bird nest i Size, if X i ＜X min Performing cross mutation treatment on the ith bird nest; otherwise, the cross mutation operation is ended;
s383: selecting a random number r for the position of the ith bird nest i ∈[0，1]If r i ＜p m The variation treatment is carried out on the bird nest position, and the method is as follows:
x i ＝x min +(x max -x min )r；
s384: if r i ＜p c Performing cross processing on the mutated bird nest positions, wherein the object of the cross operation is a global optimal solution;
s385: ending the cross mutation operation;
step 4: solving the double-layer planning model established in the step 1 and the step 2 by utilizing the improved azalea searching algorithm obtained in the step 3;
step 5: and (3) feeding back the result obtained by solving the step (4), and repeatedly iterating to obtain an optimal planning scheme.
Preferably, the specific steps of the step 4 are:
solving the upper layer model and the lower layer model established in the step 1 and the step 2 by utilizing the improved azalea searching algorithm obtained in the step 3, and firstly determining decision variables in the upper layer model and the lower layer model, namely X in the improved azalea searching algorithm i Then determining the required optimizing strategy, wherein the upper model is directly solved by MIPL toolThe method comprises the steps of carrying out a first treatment on the surface of the In the lower layer model, S i 、P DG，i And the grid structure, the DG access position and the installed capacity are obtained by solving the decision variables through a CSA algorithm.
Preferably, the specific steps of step 5 include:
feeding back the DG access position and the installed capacity obtained in the step 4 to the upper model established in the step 1, and solving the upper model again; and (5) after repeated iteration, outputting the optimal grid structure, the DG access position and the installed capacity. The beneficial effects of the invention are as follows: under the condition that the distributed energy is considered to be connected into the power distribution network, how to achieve the minimum network loss on the basis of the unchanged total investment amount and what investment scheme is used for achieving the best investment. Has certain application value.
Drawings
Fig. 1 is a flowchart of a power distribution network planning method for promoting DG digestion based on an improved azalea search algorithm according to the present invention.
FIG. 2 is a flow chart of an improved azalea search algorithm of the present invention.
Detailed Description
The invention will be further described with reference to the drawings and the specific embodiments, but the scope of the invention is not limited thereto.
As shown in fig. 1, the power distribution network planning method for promoting DG digestion based on the improved azalea search algorithm disclosed by the invention comprises the following specific implementation steps:
(1) Establishing an upper layer model by taking minimum network loss cost and minimum reliability cost (fault loss cost) of the power distribution network as objective functions:
and (3) establishing an upper layer model by taking minimum network loss cost and minimum reliability cost (fault loss cost) of the power distribution network as objective functions, determining an optimal network frame structure of the power distribution network, and providing a planning scheme for a lower layer. The model is as follows:
min C＝C loss +μ(C F +C E )
wherein: c (C) loss The total cost is annual net loss; c (C) F The power failure fault expense for the system; c (C) E The loss cost for the user caused by power failure; mu is the weight of the reliability index in the objective function. The expressions for the various costs in the objective function are as follows:
wherein: c o The cost is unit network loss; c F Punishment cost under the unit power failure times; c E Cost is lost for a unit power outage user; τ is the maximum network loss hours; n (N) l Is the total number of lines; n (N) k A number of types selectable for the ith line; s is the scene number; omega i The occurrence probability of the ith scene; p (P) loss，i The active power loss value of the ith line; i i A current value for the i-th scene; s is S i，k A "0-1" state variable, indicating that the ith line selects the kth model investment state, 1 indicating that the model was selected for investment, 0 indicating that the model was not selected for investment; r is R i，k The total resistance of the i-th line when the k-th model is selected.
In combination with the actual power distribution network planning situation, constraint conditions are as follows:
1) Total investment cost constraints
Wherein: c (C) T，i The price of the transformer newly built or reconstructed in the ith scene is calculated; and C' is the total investment cost of the distribution network.
2) Investment state constraint for candidate numbers
S i，k ∈{0，1}
3) Connectivity constraints
Each load point in the power distribution network is guaranteed to be communicated with the network, power is supplied to all load points in the area, and island conditions are avoided.
4) Tie line position constraint
The power distribution network in China is a radiation type network, so that the normal operation of the power distribution network is facilitated, and the line network frame is determined by the wiring form of the power distribution network.
(2) The method comprises the following steps of building a lower model by taking the maximum DG absorption capacity as a target:
in the scheme obtained from the model established in the step 1, a lower layer model is established by taking the maximum DG absorption capacity as a target, the calculation result is fed back to an upper layer, and then the upper layer makes an optimal decision. The model is as follows:
wherein: p (P) DG，i DG total capacity for node i, S i A "0-1" state variable indicates whether the node has access to a DG.
The constraint conditions are as follows:
1) Power distribution network tide constraint
Wherein: p (P) i An active injection power value for node i; q (Q) i The reactive power injection power value of the node i; u (U) i ，U j The voltage amplitudes of the nodes i and j are respectively; g ij ，B ij Respectively the real part and the imaginary part of the node admittance matrix; θ ij Is the phase angle difference between nodes i and j; j epsilon i is all nodes directly connected with the node i; beta i For DG availability, a rough calculation may take 1.0;is the power factor angle of DG.
2) Node voltage constraint of power distribution network
U i min ≤U i ≤U i max
Wherein: u (U) i The voltage amplitude of the node i; u (U) imin The lower limit of the voltage amplitude of the node i; u (U) imax Is the upper voltage magnitude limit of node i.
3) DG force constraint
Wherein: p (P) DG，i The DG active power value of the node i; q (Q) DG，i The DG reactive power value of the node i; p (P) DG ， imax The DG active power upper limit of the node i; q (Q) DG，imax Is the DG reactive power upper limit for node i.
4) Tidal current direction constraint
The current in the traditional power distribution network flows unidirectionally, so that the protection configuration is relatively simple, and current three-section protection of a single-side power supply is adopted. When DG is connected, the direction of feeder line tide should not be changed, otherwise, misoperation or refusal of line protection may be caused. Assuming that the distribution network adopts a downstream numbering method, the following steps are:
P ij ≥0；j＞i，j∈i
5) Access point number constraints
When planning a total capacity of the distributed energy source, a mode of accessing the power grid nearby should be adopted. Because the distance between the buses of the power distribution network is smaller, the distributed energy sources can be connected into the buses in a scattered mode or connected into a plurality of buses after being concentrated. In view of the flexibility and reliability of system operation and control, the number of access points of the distributed energy sources should not be excessive and should be less than a certain number.
Wherein: n (N) k Indicating DG maximum access point number.
(3) An improved azalea search algorithm is formed:
the improved azalea search algorithm flow is shown in figure 2. The crossover and mutation in the genetic algorithm are introduced into the traditional azalea search algorithm to form an improved azalea search algorithm. The improved azalea search algorithm is as follows:
1) Azalea birds lay one egg at a time randomly in any bird nest, which is denoted as a solution, and only one egg is in one bird nest.
2) The best eggs representing the current optimal solution will be retained to the next generation.
3) The number of bird nests n available for selection by azalea birds is constant.
4) The probability of the host mother finding a heterogeneous is P a ∈{0，1}。
5) After being found, taking the difference X between the bird nest position and the global optimal solution as the basis of the cross variation, the difference of the ith bird nest at the t generationCan be calculated by the following formula:
wherein: g t The optimal position of the t generation bird nest;the position of the ith nest at the t generation; x is x max Maximum value of bird nest position variable; x is x min Is the minimum value of the bird's nest position variable.
6) L (λ) is the Levy flight random course value, which is distributed as follows:
L(λ)～μ＝t -λ ；1＜λ≤3
wherein: u is a gravity tail power law distribution function; lambda is the exponentially related part.
7) The new solution is generated as follows:
wherein: h is the step size and h >0, typically h=1;
8) And performing cross mutation treatment on the bird nest position. The method comprises the following specific steps:
(1) Determining a lower limit X of the difference X min Mutation rate p m Crossover rate p c 。
(2) Judging X of ith bird nest i Size, if X i ＜X min Performing cross mutation treatment on the ith bird nest; otherwise, jumping to step (5).
(3) Selecting a random number r for the position of the ith bird nest i ∈[0，1]If r i ＜p m The variation treatment is carried out on the bird nest position, and the method is as follows:
x i ＝x min +(x max -x min )r
(4) If r i ＜p c And performing cross processing on the mutated bird nest positions, wherein the object of the cross operation is a global optimal solution.
(5) The crossover mutation operation ends.
(4) And (3) solving the double-layer planning model established in the step (1) and the step (2) by utilizing the improved azalea search algorithm obtained in the step (3):
solving the double-layer planning model established in the step 1 and the step 2 by utilizing the improved azalea search algorithm obtained in the step 3, and firstly determining decision variables in the model, namely X in the algorithm i And then determines the additional optimization strategy that it needs to take. The upper model can be directly solved by MIPL tool, and S in the lower model i 、P DG，i All are decision variables, and belong to the nonlinear mixed integer programming problem, so that the improved CSA algorithm is mainly utilized to solve the partial model, and a grid structure, DG access positions and installed capacity are obtained.
(5) And (3) feeding back the result obtained in the step (4), and repeatedly iterating to obtain an optimal planning scheme.
Feeding back the DG access position and the installed capacity obtained in the step 4 to the upper model established in the step 1, and solving the upper model again; and finally outputting the optimal grid structure, the DG access position and the installed capacity through repeated iteration.
The invention is further described by way of a specific example. The basic model of the circuit can be reversely pushed according to the circuit impedance parameter and the common distribution circuit model parameter. The general distribution line model parameters IEEE33 node system line models are shown in tables 1 and 2, respectively.
Table 1 commonly used distribution line model and parameters thereof
TABLE 2 IEEE33 node System part line model parameters
For the reliability index, the following assumptions are made:
1) If a new model is invested in a certain line, the number of annual blackouts and annual blackouts hours are both 0.
2) If a certain line does not invest in a new model, the annual power failure times and annual power failure hours are respectively 1 and 0.5.
3) If a certain line has a subsequent line, the annual power failure times caused by the non-investment of the line is 1 plus the number of the subsequent lines.
4) Other parameters in the model are shown in table 3:
table 3 parameter values in planning models
In the system, a total of 2020kW renewable energy source is planned, and in the multi-point access system, it is assumed that the number of access points is not more than 4, and the access points must not be 31,1,2,3,4,5 nodes. The existing power grid is improved by 100 ten thousand yuan, and the current power distribution network is upgraded and improved, so that the optimal investment effect is obtained. By adopting the algorithm and utilizing Matlab programming, the results are shown in tables 4-5 after calculation:
TABLE 4 route improvement planning results
TABLE 5 New energy Access Condition
Further calculation is carried out, the total cost of the system network loss and the reliability before planning and transformation is 97.63 ten thousand yuan, the total cost of the system network loss and the reliability after upgrading and transformation is 39.52 ten thousand yuan, namely the total cost of the power distribution network can be reduced by 58.11 ten thousand yuan after upgrading and transformation, and the total cost of investment transformation is 100 ten thousand yuan, namely the investment cost recovery period is less than 2 years.
The foregoing description is merely a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any obvious modifications, substitutions or variations can be made by those skilled in the art without departing from the spirit of the present invention. Therefore, the application scope of the present invention should be subject to the application scope of the claims.

Claims (3)

1. The power distribution network planning method for promoting DG digestion is characterized by comprising the following steps of:
step 1: establishing an upper layer model by taking the minimum fault loss cost as an objective function, wherein the fault loss cost comprises the network loss cost and the reliability cost of the power distribution network; the method specifically comprises the following steps: determining an optimal grid structure of the distribution network, and providing a planning scheme for a lower layer, wherein the upper layer model is as follows:
minC＝C loss +μ(C F +C E )
wherein: c is the cost of failure loss, C loss The total cost is annual net loss; c (C) F The power failure fault expense for the system; c (C) E The loss cost for the user caused by power failure; μ is the weight occupied by the reliability index in the objective function, and the expressions of various fees in the objective function are as follows:
wherein: c o The cost is unit network loss; c F Punishment cost under the unit power failure times; c E Cost is lost for a unit power outage user; τ is the maximum network loss hours; n (N) l Is the total number of lines; n (N) k A number of types selectable for the ith line; s is the scene number; omega i The occurrence probability of the ith scene; p (P) loss，i The active power loss value of the ith line; i i A current value for the i-th scene; s is S i,k A "0-1" state variable, indicating that the ith line selects the kth model investment state, 1 indicating that the model was selected for investment, 0 indicating that the model was not selected for investment; r is R i,k The total resistance value of the ith line when the kth model of the ith line is selected;
the constraint conditions of the established upper layer model comprise:
1) Total investment cost constraints
Wherein: c (C) T，i The price of the transformer newly built or reconstructed in the ith scene is calculated; c' is the total investment cost of the distribution network;
2) Investment state constraint for candidate numbers
S i,k ∈{0,1}
3) Connectivity constraints
Each load point in the power distribution network is guaranteed to be communicated with a network, power is supplied to all load points in the area, and island conditions are avoided;
4) Tie line position constraint
The line net rack is determined by the wiring form of the power distribution network;
step 2: the method for constructing the lower model by taking the maximum DG absorption capacity as a target specifically comprises the following steps:
according to the upper model established in the step 1, establishing a lower model with the maximum DG absorption capacity as a target, feeding back a calculation result of the lower model to the upper model, and making an optimal decision by the upper model; the lower model is as follows:
wherein: c (C) R To the digestion ability of DG, P DG，i DG total capacity for node i, S i A "0-1" state variable, indicating whether the node has access to a DG;
the constraint conditions of the lower model include:
1) Power distribution network tide constraint
Wherein: p (P) i An active injection power value for node i; q (Q) i The reactive power injection power value of the node i; u (U) i ,U j The voltage amplitudes of the nodes i and j are respectively; g ij ,B ij Respectively the real part and the imaginary part of the node admittance matrix; θ ij Is the phase angle difference between nodes i and j; j epsilon i is all nodes directly connected with the node i; beta i For DG availability, a rough calculation may take 1.0;power factor angle for DG;
2) Node voltage constraint of power distribution network
U imin ≤U i ≤U imax
Wherein: u (U) i The voltage amplitude of the node i; u (U) imin The lower limit of the voltage amplitude of the node i; u (U) imax The upper limit of the voltage amplitude of the node i;
3) DG force constraint
Wherein: p (P) DG，i The DG active power value of the node i; q (Q) DG，i The DG reactive power value of the node i; p (P) DG，imax The DG active power upper limit of the node i; q (Q) DG，imax The DG reactive power upper limit of the node i;
4) Tidal current direction constraint
After DG is connected into the power distribution network, the direction of feeder line power flow is not changed, and if the power distribution network adopts a downstream numbering method, the following steps are adopted:
P ij ≥0；j＞i，j∈i；
5) Access point number constraints
When the total capacity of the planned distributed energy source is fixed, a mode of accessing the power grid nearby is adopted, and the number of access points of DGs is smaller than a certain number, which is expressed as:
wherein: n (N) k Representing DG maximum access point number;
step 3: the crossover and mutation in the genetic algorithm are introduced into the azalea search algorithm to form an improved azalea search algorithm, which is specifically as follows:
1) Rhododendrons lay one egg in any bird nest at a time randomly, which is marked as a solution, and only one egg is in one bird nest;
2) The best eggs representing the current optimal solution will be retained to the next generation;
3) The number n of bird nests for the azalea to select is certain;
4) The probability of the host mother finding a heterogeneous is P a ∈{0,1}；
5) After being found, taking the difference X between the bird nest position and the global optimal solution as the basis of the cross variation, the difference of the ith bird nest at the t generationCan be calculated by the following formula:
wherein: g t The optimal position of the t generation bird nest;the position of the ith nest at the t generation; x is x max Maximum value of bird nest position variable; x is x min Is the minimum value of the bird nest position variable;
6) L (λ) is the Levy flight random course value, which is distributed as follows:
L(λ)～μ＝t -λ ；1＜λ≤3
wherein: u is a gravity tail power law distribution function, and lambda is an exponential correlation part;
7) The new solution is generated as follows:
wherein: h is the step size and h >0;
8) The method carries out cross mutation treatment on the bird nest position, and comprises the following substeps:
s381: determining a lower limit X of the difference X min Mutation rate p m Crossover rate p c ；
S382: judging X of ith bird nest i Size, if X i ＜X min Performing cross mutation treatment on the ith bird nest; otherwise, the cross mutation operation is ended;
s383: selecting a random number r for the position of the ith bird nest i ∈[0,1]If r i ＜p m The variation treatment is carried out on the bird nest position, and the method is as follows:
x i ＝x min +(x max -x min )r；
s384: if r i ＜p c Performing cross processing on the mutated bird nest positions, wherein the object of the cross operation is a global optimal solution;
s385: ending the cross mutation operation;
step 4: solving the double-layer planning model established in the step 1 and the step 2 by utilizing the improved azalea searching algorithm obtained in the step 3;
step 5: and (3) feeding back the result obtained by solving the step (4), and repeatedly iterating to obtain an optimal planning scheme.
2. The power distribution network planning method for promoting DG digestion according to claim 1, wherein the specific steps of step 4 are as follows:
utilization stepSolving the upper layer model and the lower layer model established in the step 1 and the step 2 by the improved azalea searching algorithm obtained in the step 3, and firstly determining decision variables in the upper layer model and the lower layer model, namely X in the improved azalea searching algorithm i Then determining a required optimizing strategy, wherein an upper model is directly solved by using an MIPL tool; in the lower layer model, S i 、P DG，i And the grid structure, the DG access position and the installed capacity are obtained by solving the decision variables through a CSA algorithm.
3. The power distribution network planning method for promoting DG digestion according to claim 1, wherein the specific steps of step 5 are:
feeding back the DG access position and the installed capacity obtained in the step 4 to the upper model established in the step 1, and solving the upper model again; and (5) after repeated iteration, outputting the optimal grid structure, the DG access position and the installed capacity.
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