CN113361805B - Power distribution network planning method and system - Google Patents

Power distribution network planning method and system Download PDF

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CN113361805B
CN113361805B CN202110735473.6A CN202110735473A CN113361805B CN 113361805 B CN113361805 B CN 113361805B CN 202110735473 A CN202110735473 A CN 202110735473A CN 113361805 B CN113361805 B CN 113361805B
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赵立军
徐明忻
金国锋
邢敬舒
王姣
孙永辉
刘自发
李颉雨
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State Grid Corp of China SGCC
North China Electric Power University
Economic and Technological Research Institute of State Grid Inner Mongolia Electric Power Co Ltd
State Grid Eastern Inner Mongolia Power Co Ltd
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North China Electric Power University
Economic and Technological Research Institute of State Grid Inner Mongolia Electric Power Co Ltd
State Grid Eastern Inner Mongolia Power Co Ltd
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Abstract

The invention relates to a power distribution network planning method and system. The method comprises the following steps: constructing a power grid planning model based on the active output power of the distributed power supply of each node, the output power of the photovoltaic generator of each node, the output power of the wind driven generator of each node, the electric power of the direct heater and the electric power of the heat storage heater; solving the power grid planning model by adopting a particle swarm algorithm to obtain an optimal power grid planning scheme; the planning scheme comprises the output power of the photovoltaic generator and the output power of the wind driven generator of each node. The invention can make the planning scheme better adapt to the influence of the access of electric heating on the power grid, and effectively ensure the power supply reliability and the power quality of the large-scale access of the electric heating to the power distribution network.

Description

Power distribution network planning method and system
Technical Field
The invention relates to the technical field of power distribution network planning, in particular to a power distribution network planning method and system.
Background
With the continuous development of economy, the demands of residents, commercial tenants, industries and the like on electric energy are increasingly promoted, and the problems of energy consumption and environmental pollution are also promoted. To solve such problems, if the power generation is started only from the power generation supply side, the energy structure is optimized by increasing the power generation ratio of clean energy, and the effect is very limited. The electric quantity replaced by electric energy mainly comes from renewable energy sources for power generation and a part of ultra-low emission coal-electric machine sets, so that the electric energy replacement is an important means for clean terminal energy consumption.
Compared with the traditional power generation, the distributed power supply has the advantages of obvious cleanness, no pollution, system loss reduction, transmission cost reduction, electric energy quality improvement, power supply reliability improvement and the like. However, the access of the distributed power supply enables the traditional power distribution network to be more energy-saving, environment-friendly, flexible and convenient, and simultaneously enables the operation and planning of the traditional power distribution network to be changed insignificantly.
The traditional power distribution system generally has typical characteristics of radiation, unidirectional power flow and the like, the access of a distributed power supply causes a plurality of power supplies to appear in a network, if the distributed power supply is reasonably configured, the system can be helped to reduce network loss and improve voltage level, and otherwise, the electric energy quality and the power supply safety of the system are reduced. According to the load prediction result, the limitations of transformer substation capacity, branch capacity, voltage level, network structure, reliability and the like are comprehensively considered, and a planning scheme can be formulated for the traditional power distribution network, so that the traditional power distribution network planning belongs to the deterministic optimization problem. However, distributed power supply output power is not constant throughout the day, and is characterized by uncertainty, randomness, and intermittency. Due to the factors, the load of the power distribution network is difficult to be accurately predicted, and then the distribution network planning is converted into an uncertain optimization problem. This will undoubtedly greatly increase the difficulty of planning the distribution network, and may even bring about a series of problems, such as the planning scheme is not in accordance with the reality and the scheme cost is too high. With the popularization and variety of distributed power sources, the problem of power distribution network planning becomes more troublesome.
In addition, electric energy replacement projects are gradually connected to the power grid in a large scale. Typical electric energy substitution technologies include electric heating, heat pumps, electric kilns, electric (regenerative) boilers, and electric cold storage air conditioners. Among them, the electric heating is gradually and widely applied to daily heating of various buildings such as schools, hospitals, office buildings, residences, markets, supermarkets, factory workshops and the like due to the advantages of water and land conservation, uniform heating, excellent safety performance, building space resource saving, flexible control and the like. Electric heating also gradually becomes a novel load in the power system, and becomes a novel influence factor in the planning process of the power distribution network.
Based on the current situation of distributed power sources and electric heating and analysis of the influence on the power grid, the traditional power grid planning model is too single, few influences on novel influence factors are considered, the power distribution network planning under the current new load background cannot be adapted, the energy demand of current users is difficult to meet, and therefore how to scientifically plan the power distribution network is the problem to be solved by the current power grid.
Disclosure of Invention
The invention aims to provide a power distribution network planning method and system, which can enable a planning scheme to better adapt to the influence of the access of electric heating on a power grid, and effectively ensure the power supply reliability and the power quality of the large-scale access of the electric heating to the power distribution network.
In order to achieve the purpose, the invention provides the following scheme:
a power distribution network planning method comprises the following steps:
obtaining model parameters in a power grid; the model parameters comprise active output power of the distributed power supply of each node, output power of the photovoltaic generator of each node, output power of the wind driven generator of each node, electric power of the direct heater and electric power of the heat storage heater;
constructing a power grid planning model based on the model parameters;
solving the power grid planning model by adopting a particle swarm algorithm to obtain an optimal power grid planning scheme; the planning scheme comprises the output power of the photovoltaic generator and the output power of the wind driven generator of each node; the sum of parameters corresponding to the optimal power grid planning scheme is minimum; the sum of the parameters is the sum of network loss, voltage stability and voltage deviation.
Optionally, the power grid planning model is:
Figure BDA0003141477900000021
Figure BDA0003141477900000022
Figure BDA0003141477900000031
Figure BDA0003141477900000032
Figure BDA0003141477900000033
U i,min ≤U i ≤U i,max
x DWG,i S 0 +x PV,i S 0 ≤S DG,i,max
0＜P d ≤P h
0＜P c ≤P h wherein f is fitness, N is the total number of nodes of the power distribution network, and P is ij Representing the active power, Q, flowing through the end of branch ij ij Representing the reactive power, U, flowing through the ends of the branches ij j Representing the voltage magnitude of node j, R ij Resistance, k, representing branch ij ij Represents the state quantity, m, of branch ij ij Indicating the voltage stabilization of branch ij, U i Representing the voltage magnitude, U, of node i i,N Indicating the nominal voltage, P, of node i j Representing the active power, X, flowing into node j ij Representing the reactance, Q, of branch ij j Representing reactive power, P, flowing into node i i Representing the active power, P, flowing into node i DG,i Representing the active output power, P, of the distributed power supply of node i Load,i Representing the total equivalent active load of node i, G ij Denotes the conductance, delta, of branch ij ij Representing the difference between the phase angles of the voltages at node i and node j, S ij,min Representing the lower limit, S, of the apparent power flowing on branch ij ij,max Representing the upper limit, U, of the apparent power flowing on branch ij i,min Represents the lower limit, U, of the voltage amplitude of node i i,max Representing the upper limit, x, of the voltage magnitude of node i DWG,i Output power of a wind turbine representing node i, S 0 Denotes unit capacity, x PV,i Output power of photovoltaic generator, S, representing node i DG,i,max Representing the distribution at the ith nodeUpper limit of power supply installation capacity, P d Representing the electric power of the direct heater, P h Indicating rated power, P, of electric heating c Indicating the electric power of the heat storage and heating device.
Optionally, the solving of the power grid planning model by using the particle swarm algorithm to obtain an optimal power grid planning scheme specifically includes:
under the nth iteration number, obtaining a power grid planning scheme set under the current iteration number according to the parameter set under the last iteration number and the inertia weight under the current iteration number; the parameter set comprises a particle updating speed, a power grid planning scheme set, an individual extreme value and a group extreme value;
obtaining the voltage amplitude, the inflow active power and the inflow reactive power of each node in each power grid planning scheme in the power grid planning scheme set under the current iteration times according to the power grid planning scheme set and the load flow calculation formula under the current iteration times;
inputting the voltage amplitude of each node, the inflow active power and the inflow reactive power into the power grid planning model to obtain the fitness of each power grid planning scheme in the power grid planning scheme set under the current iteration times;
taking the power grid planning scheme with the minimum fitness in the current iteration times and the power grid planning scheme with the minimum fitness in the individual extreme values of the previous iteration times as the individual extreme values of the current iteration times;
taking the power grid planning scheme with the minimum fitness in the power grid planning scheme with the minimum fitness under the previous iteration times and the power grid planning scheme with the minimum fitness in the group extreme value of the previous iteration times as the group extreme value of the current iteration times;
judging whether the current iteration times reach the set iteration times or not to obtain a first judgment result;
if the first judgment result is yes, determining the group extreme value under the current iteration number as an optimal power grid planning scheme;
and if the first judgment result is negative, entering the next iteration and updating the inertia weight.
Optionally, the obtaining of the power grid planning scheme set under the current iteration number according to the parameter set under the last iteration number and the inertia weight under the current iteration number specifically includes:
according to the formula
Figure BDA0003141477900000041
And the power grid planning scheme set under the current iteration number is obtained, wherein,
Figure BDA0003141477900000042
represents the kth power grid planning scheme in the power grid planning scheme set under iter +1 iteration times,
Figure BDA0003141477900000043
represents the kth power grid planning scheme in the power grid planning scheme set under iter iteration times, w represents the inertia weight under iter +1 iteration times,
Figure BDA0003141477900000044
representing the particle update speed of the kth grid planning scheme in the set of grid planning schemes under iter iteration times, c 1 Representing individual extremum coefficients, rand () representing a random function, P best Representing individual extrema at iter iterations, c 2 Represents the coefficient of the population extremum, G best Representing the population extremum at iter iterations.
Optionally, before obtaining the voltage amplitude, the incoming active power, and the incoming reactive power of each node in each power grid planning scheme in the power grid planning scheme set under the current iteration number according to the power grid planning scheme set and the power flow calculation formula under the current iteration number, the method further includes:
judging whether a power grid planning scheme in the power grid planning scheme set under the current iteration times is not in a set range or not to obtain a second judgment result;
and if the second judgment result is yes, performing variation operation on the power grid planning scheme which is not in the set range to obtain a varied power grid planning scheme set under the current iteration times.
Optionally, the step of taking the power grid planning scheme with the minimum fitness in the current iteration times and the power grid planning scheme with the minimum fitness in the individual extreme value of the previous iteration times as the individual extreme value of the current iteration times specifically includes:
under the k-th traversal, judging whether the fitness in the power grid planning scheme under the current traversal times in the power grid planning scheme set under the current iteration times is smaller than the fitness of the individual extreme value of the previous iteration times to obtain a third judgment result;
if the third judgment result is negative, determining that the individual extreme value of the previous iteration number is a candidate scheme under the current traversal number; if the third judgment result is yes, determining the power grid planning scheme under the current traversal times as a candidate scheme under the current traversal times;
judging whether the power grid planning scheme in the power grid planning scheme set under the current iteration number is traversed or not to obtain a fourth judgment result;
if the fourth judgment result is negative, updating the traversal times and entering next traversal;
and if the fourth judgment result is yes, determining that the scheme to be selected under the current traversal times is the individual extreme value of the current iteration times.
A power distribution network planning system, comprising:
the acquisition module is used for acquiring model parameters in the power grid; the model parameters comprise active output power of the distributed power supply of each node, output power of the photovoltaic generator of each node, output power of the wind driven generator of each node, electric power of the direct heater and electric power of the heat storage heater;
the model construction module is used for constructing a power grid planning model based on the model parameters;
the scheme determining module is used for solving the power grid planning model by adopting a particle swarm algorithm to obtain an optimal power grid planning scheme; the planning scheme comprises the output power of the photovoltaic generator of each node and the output power of the wind driven generator; the sum of the parameters corresponding to the optimal power grid planning scheme is minimum; the sum of the parameters is the sum of network loss, voltage stability and voltage deviation.
Optionally, the scheme planning module includes:
the updating unit is used for obtaining a power grid planning scheme set under the current iteration times according to the parameter set under the last iteration times and the inertia weight under the current iteration times under the nth iteration times; the parameter set comprises a particle updating speed, a power grid planning scheme set, an individual extreme value and a group extreme value;
the load flow calculation unit is used for obtaining the voltage amplitude, the inflow active power and the inflow reactive power of each node in each power grid planning scheme in the power grid planning scheme set under the current iteration times according to the power grid planning scheme set and the load flow calculation formula under the current iteration times;
the fitness calculation unit is used for inputting the voltage amplitude of each node, the inflow active power and the inflow reactive power into the power grid planning model to obtain the fitness of each power grid planning scheme in the power grid planning scheme set under the current iteration times;
the individual extreme value determining unit is used for taking the power grid planning scheme with the minimum fitness under the current iteration times and the power grid planning scheme with the minimum fitness in the individual extreme values of the previous iteration times as the individual extreme values of the current iteration times;
the group extreme value determining unit is used for taking the power grid planning scheme with the minimum fitness under the previous n times of iteration times and the power grid planning scheme with the minimum fitness in the group extreme value of the previous iteration times as the group extreme value of the current iteration times;
the first judgment unit is used for judging whether the current iteration times reach the set iteration times or not to obtain a first judgment result;
the scheme determining unit is used for determining the group extreme value under the current iteration number as the optimal power grid planning scheme if the first judgment result is yes;
and the updating iteration unit is used for entering the next iteration and updating the inertia weight if the first judgment result is negative.
Optionally, the scheme planning module further includes:
the second judging unit is used for judging whether the power grid planning scheme in the power grid planning scheme set under the current iteration times is not in the set range or not to obtain a second judging result;
and if the second judgment result is yes, performing variation operation on the power grid planning scheme which is not in the set range to obtain a varied power grid planning scheme set under the current iteration times.
Optionally, the individual extremum determining unit includes:
the third judgment subunit is configured to, in the kth traversal, judge whether the fitness in the power grid planning scheme of the current traversal number in the power grid planning scheme set of the current iteration number is smaller than the fitness of the individual extreme value of the previous iteration number, and obtain a third judgment result;
a candidate scheme determining subunit, configured to determine, if the third determination result is negative, that the individual extremum of the previous iteration number is the candidate scheme in the current traversal number; if the third judgment result is yes, determining the power grid planning scheme under the current traversal times as a candidate scheme under the current traversal times;
the fourth judging subunit is configured to judge whether the power grid planning scheme in the power grid planning scheme set under the current iteration number is completely traversed to obtain a fourth judging result;
the next traversal subunit is used for updating the traversal times and entering next traversal if the fourth judgment result is negative;
and the individual extreme value determining subunit is configured to determine that the to-be-selected scheme under the current traversal number is the individual extreme value of the current iteration number if the fourth determination result is yes.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the invention considers the novel factors of the active output power of the distributed power supply of each node, the output power of the photovoltaic generator of each node, the output power of the wind driven generator of each node, the electric power of the direct heater and the electric power of the heat storage heater in constructing the model, so that the planning scheme can better adapt to the influence of the access of electric heating on the power grid, the power supply reliability and the electric energy quality of the large-scale access of the electric heating to the power distribution network are effectively ensured, and the method has important significance in improving the aspects of future electric energy substitution and the large-scale access of the distributed power supply to the power distribution network, ensuring the power supply reliability and the electric energy quality and the like.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of a power distribution network planning method according to an embodiment of the present invention;
fig. 2 is a schematic power flow diagram of a portion of a radial power distribution network including distributed power sources according to an embodiment of the present invention;
fig. 3 is a schematic partial power flow diagram of a radial power distribution network according to an embodiment of the present invention;
fig. 4 is a flowchart for solving a power grid planning model by applying a particle swarm algorithm according to the embodiment of the present invention;
fig. 5 is a structural diagram of a power distribution network planning system according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention more comprehensible, the present invention is described in detail with reference to the accompanying drawings and the detailed description thereof.
The embodiment provides a power distribution network planning method, as shown in fig. 1, the method includes:
step 101: obtaining model parameters in a power grid; the model parameters comprise active output power of the distributed power supply of each node, output power of the photovoltaic generator of each node, output power of the wind driven generator of each node, electric power of the direct heater and electric power of the heat storage heater.
Step 102: and constructing a power grid planning model based on the model parameters.
Step 103: and solving the power grid planning model by adopting a particle swarm algorithm to obtain an optimal power grid planning scheme. The planning scheme comprises the output power of the photovoltaic generator and the output power of the wind driven generator of each node.
In practical application, the output power of wind power generation is greatly influenced by wind speed, the wind speed is closely related to weather conditions, and the variable weather conditions cause the wind speed to be different in different years, different seasons and even different time in the same day, which brings great challenge to prediction work. The invention adopts Weibull distribution to the wind speed model, the wind speed change characteristic in a certain period can be expressed by using bifunction Weibull distribution, and the output power probability density function f of wind power generation DWG (v) The formula is as follows:
Figure BDA0003141477900000081
wherein v is the wind speed at the hub of the fan impeller; c. k is the scale parameter and the shape parameter of the two-parameter Weibull distribution respectively, wherein
Figure BDA0003141477900000091
δ v 、μ v Respectively representing the mean value and the standard deviation of historical wind speed data in corresponding time periods; Γ is the gamma function.
Output power P of wind driven generator DWG The output power meter of the wind power generator of the node i has correlation with the actual wind speed v and is expressed as the following segmentation function expressionShown as x DWG,i ：
Figure BDA0003141477900000092
Wherein, P DWG_r Rated output power of the fan; v. of ci The wind speed is cut in; v. of cr Rated wind speed; v. of co To cut out the wind speed.
The output power of photovoltaic power generation is related to random factors such as the irradiation intensity of sunlight and the ambient temperature, wherein the output power is most closely related to the irradiation intensity. Within a certain period of time, the solar illumination intensity s approximately follows Beta distribution, and the probability density function f of the output power of the photovoltaic power generation PV The formula(s) is as follows:
Figure BDA0003141477900000093
wherein s is the actual illumination intensity; s max Maximum illumination intensity;
Figure BDA0003141477900000094
is defined as a clear sky factor; alpha and Beta are two parameters of Beta distribution, wherein
Figure BDA0003141477900000095
Figure BDA0003141477900000096
Wherein, mu s 、δ s Respectively is the average value and the standard deviation of the historical clear sky factors in the corresponding time period.
Output power P of photovoltaic generator PV The method has a correlation with the illumination intensity s, the piecewise function expression is as follows, and the photovoltaic output power of the node i is represented as x PV,i ：
Figure BDA0003141477900000097
Wherein s is r Is the rated illumination intensity; p PV_r The rated output power of photovoltaic power generation.
The wind power and photovoltaic output power are integrated, and the active output power of the distributed power supply of the node i is represented as P DG,i ＝x PV,i +x DWG,i 。
The Gaussian distribution is a probability distribution model widely used for describing randomness of a conventional load, but the actual load size has a certain range, so that a truncated Gaussian distribution model is adopted to simulate the change of the actual power load in a certain period, and a probability density function distribution formula f of the model is L (P L ) The regular load of node i is denoted P as follows L,i ：
Figure BDA0003141477900000101
Wherein, P L Is the load power; mu.s L 、δ L Respectively representing the mean value and the standard deviation of historical data of the load size in the corresponding time period; p is up 、P low Respectively, the upper and lower thresholds of the load. According to probability density function distribution f L (P L ) Sampling is carried out to obtain the load power P L 。
The operation strategy adopted by the heat accumulating type electric heating is as follows: in the valley period of the electricity price, the heater is directly used for heating to supply heat for users; meanwhile, the heat storage heater is used for storing the heat after heating until the heat storage device is full; in the peak time period of the electricity price, the heat storage device firstly releases heat to supply heat for users; and if the heat of the heat storage device is not enough to meet the heat load requirement of the user, starting the direct-heating equipment to supply heat in an auxiliary manner. The electric power of each power consuming module is expressed as follows.
(1) Electric power P of direct heater d
Figure BDA0003141477900000102
Wherein, t f0 、t f Respectively as the starting time and the ending time of the peak electricity price; h load,t The actual heat load requirement of the electric heating user at the moment t is met; s. the t The heat storage capacity of the heat storage device at the moment t; eta 0 The heat efficiency of the direct heater is improved. Direct heater electrical power for node i is denoted P d,i 。
(2) Electric power P of heat storage heater c
Figure BDA0003141477900000111
Wherein, t g0 、t g Respectively the starting time and the ending time of the valley electricity price; c e Storing heat power for the heat storage and heating module; s max The upper limit value of the heat storage capacity of the heat storage device; eta 1 The heating efficiency of the heat storage heater is improved. The electrical power of the heat storage and heating device of node i is denoted as P c,i 。
The load conditions of the two heaters are combined, and the electric heating load of the node i is expressed as P heat,i ＝P d,i +P c,i . And combining the output power of the conventional load to obtain the total equivalent load P of the node i Load,i ＝P L,i +P heat,i ＝P L,i +P d,i +P c,i 。
Distributed power supply output power model (P) considering time sequence characteristics DWG And P PV ) And normal load P L Electric heating load model (P) d And P c ) The method can accurately depict the conventional load and the electric heating load of each node of the power distribution network at each moment and the distributed power supply output power of each distributed power supply installation node. The model enables equivalent loads of all nodes in the power distribution network to realize time sequence expansion and accuracy improvement, and accurate and actual equivalent loads are undoubtedly necessary conditions and important bases for obtaining a feasible power distribution network planning scheme subsequently.
Construction of a network loss objective function f 1 Stability f of the distribution network 2 And voltage deviation target f 3 。
Different distribution line network wiring can lead to different power flow distributions of the distribution network, and consequently, the network loss of the distribution system can be changed. An objective function is established with minimum network loss, the smaller the objective is, the better the system is,
Figure BDA0003141477900000112
wherein N is the total number of nodes of the power distribution network; i is the node number in the power distribution network; r is ij Resistance for branch ij; k is a radical of ij Is the state quantity (k) of branch ij ij =0 for branch off, k ij =1 for branch closed) P ij 、Q ij Respectively the active power and the reactive power flowing through the tail end of the branch ij; u shape j Is the voltage magnitude at node j. f. of 2 ＝min{-m 11 ,-m 12 ,…,-m ij In which m is ij For the voltage stabilization condition of any branch ij in the power distribution network, the calculation formula is as follows:
Figure BDA0003141477900000113
wherein i and j are respectively the head and end nodes of the branch, P j +jQ j Load flowing through node j; r is ij +jX ij Is the branch impedance; u shape i Is the node voltage amplitude, X ij Is the branch reactance.
In order to ensure the voltage stability of the distribution network, m is satisfied ij Less than or equal to 1, the voltage stability of the whole distribution network is determined by the maximum value of the branch voltage stability, f 2 The smaller the value, the more stable the distribution network.
In the normal operation mode of the system, the percentage of the difference between the actual voltage of a certain node and the nominal voltage of the system to the nominal voltage of the system is called the voltage deviation of the node. The voltage deviation index is only related to the voltage amplitude, and the calculation formula is as follows:
Figure BDA0003141477900000121
wherein N is the total number of nodes of the system; u shape i Is the actual voltage at node i; u shape i,N The rated voltage of the node i is a fixed value; f. of 3 The smaller the size, the longer the service life of the line equipment.
For processing multiple targets, a direct addition method f = f is adopted 1 +f 2 +f 3 And f is the final fitness function.
By combining the analysis of the output power of the distributed power supply, the conventional load and the time sequence characteristic and considering the objective constraint of the important parameters in the objective function in the power distribution network, the constraint conditions related to the power grid planning model considering the distributed power supply and the electric heating in the embodiment are obtained, wherein the constraint conditions include the power balance constraint of the system, the branch capacity constraint, the node voltage constraint, the radial operation constraint of the power distribution network, the distributed power supply capacity constraint and the electric heating related constraint.
(1) Constraint of power balance
Figure BDA0003141477900000122
Figure BDA0003141477900000123
Wherein, P i 、Q i The active power and the reactive power of the inflow of the node i are respectively; u shape i 、U j The voltage amplitudes of the nodes i and j are respectively; g ij 、B ij Respectively the conductance and susceptance of the branch ij; delta ij Is the difference between the voltage phase angles of nodes i, j. P DG,i 、Q DG,i The distributed power supply active and reactive output power Q of the node i DG,i By power factor
Figure BDA0003141477900000124
To obtain the result of the above-mentioned method,
Figure BDA0003141477900000125
the power factor is generally taken to be constant
Figure BDA0003141477900000126
P Load,i 、Q Load,i For the total equivalent active and reactive loads of the node i, the value taking method is similar to that of the distributed power supply,
Figure BDA0003141477900000127
wherein the content of the first and second substances,
Figure BDA0003141477900000128
representing the power factor of the load.
(2) Constraint of inequality
Sequentially and respectively: branch capacity constraint S ij,min ≤S ij ≤S ij,max Node voltage constraint U i,min ≤U i ≤U i,max Capacity constraint of distributed power source S DG,i ≤S DG,i,max Real-time operation constraint of electric heating is more than 0 and less than P d ≤P h ，0＜P c ≤P h 。
Wherein S is ij 、S ij,max 、S ij,min The apparent power (branch capacity) flowing on branch ij and its upper and lower limits, respectively.
Figure BDA0003141477900000131
U i 、U i,max 、U i,min The voltage amplitude of the node i and the highest and lowest allowable voltages thereof are respectively; s DG,i 、S DG,i,max Respectively installing capacity and capacity upper limit of the distributed power supply at the ith node to be selected,
Figure BDA0003141477900000132
P h the rated power of the electric heating is adopted.
To sum up, the obtained power grid planning model is as follows:
Figure BDA0003141477900000133
Figure BDA0003141477900000134
Figure BDA0003141477900000135
Figure BDA0003141477900000136
Figure BDA0003141477900000137
U i,min ≤U i ≤U i,max
x DWG,i S 0 +x PV,i S 0 ≤S DG,i,max
0＜P d ≤P h
0＜P c ≤P h wherein f is fitness, N is the total number of nodes of the power distribution network, and P ij Representing the active power, Q, flowing through the end of branch ij ij Representing the reactive power, U, flowing through the ends of the branches ij j Representing the magnitude of the voltage at node j, R ij Representing the resistance, k, of branch ij ij Represents the state quantity, m, of branch ij ij Indicating the voltage stabilization of branch ij, U i Representing the voltage magnitude, U, of node i i,N Indicating the nominal voltage, P, of node i j Representing the active power, X, flowing into node j ij Representing the reactance, Q, of branch ij j Representing reactive power, P, flowing into node i i Representing the active power, P, flowing into node i DG,i Representing the active output power, P, of the distributed power supply of node i Load,i Representing the total equivalent active load of node i, G ij Denotes the conductance, delta, of branch ij ij Representing the difference between the phase angles of the voltages at nodes i, j, S ij,min Representing the lower limit, S, of the apparent power flowing on branch ij ij,max Representing the upper limit, U, of the apparent power flowing on branch ij i,min Represents the lower limit, U, of the voltage amplitude of node i i,max Represents the upper limit, x, of the voltage magnitude of node i DWG,i Output power of a wind turbine representing node i, S 0 Is expressed in unit capacity, x PV,i Output power of the photovoltaic generator, S, representing node i DG,i,max Represents the upper limit, P, of the installation capacity of the distributed power supply at the ith node d Representing the electric power of the direct heater, P h Indicating the rated power, P, of the electric heating c Indicating the electric power of the heat storage and heating device.
In practical application, the solving of the power grid planning model by using the particle swarm algorithm to obtain an optimal power grid planning scheme specifically includes:
under the nth iteration number, obtaining a power grid planning scheme set under the current iteration number according to the parameter set under the last iteration number and the inertia weight under the current iteration number; the parameter set comprises a particle updating speed, a power grid planning scheme set, an individual extremum and a group extremum.
And obtaining the voltage amplitude, the inflow active power and the inflow reactive power of each node in each power grid planning scheme in the power grid planning scheme set under the current iteration times according to the power grid planning scheme set and the load flow calculation formula under the current iteration times.
And inputting the voltage amplitude of each node, the inflow active power and the inflow reactive power into the power grid planning model to obtain the fitness of each power grid planning scheme in the power grid planning scheme set under the current iteration number.
And taking the power grid planning scheme with the minimum fitness in the current iteration times and the power grid planning scheme with the minimum fitness in the individual extreme values of the previous iteration times as the individual extreme values of the current iteration times.
And taking the power grid planning scheme with the minimum fitness in the last iteration times and the power grid planning scheme with the minimum fitness in the group extreme values of the last iteration times as the group extreme value of the current iteration times.
And judging whether the current iteration times reach the set iteration times to obtain a first judgment result.
And if the first judgment result is yes, determining the group extreme value under the current iteration number as an optimal power grid planning scheme.
And if the first judgment result is negative, updating to enter next iteration and updating the inertia weight.
In practical application, the obtaining of the power grid planning scheme set under the current iteration number according to the parameter set under the last iteration number and the inertia weight under the current iteration number specifically includes:
according to the formula
Figure BDA0003141477900000151
Obtaining a power grid planning scheme set under the current iteration times, wherein,
Figure BDA0003141477900000152
represents the kth power grid planning scheme in the power grid planning scheme set under iter +1 iteration times,
Figure BDA0003141477900000153
represents the kth power grid planning scheme in the power grid planning scheme set under iter iteration times, w represents the inertia weight under iter +1 iteration times,
Figure BDA0003141477900000154
representing the particle update speed of the kth grid planning scheme in the set of grid planning schemes under iter iteration times, c 1 Representing individual extremum coefficients, rand () representing a random function, P best Representing individual extrema at iter iterations, c 2 Representing the coefficient of the extremum of the population, G best Representing the population extremum at iter iterations.
In practical application, before obtaining the voltage amplitude, the incoming active power and the incoming reactive power of each node in each power grid planning scheme in the power grid planning scheme set under the current iteration number according to the power grid planning scheme set and the power flow calculation formula under the current iteration number, the method further comprises the following steps:
and judging whether the power grid planning scheme in the power grid planning scheme set under the current iteration times is not in a set range, and obtaining a second judgment result.
And if the second judgment result is yes, performing variation operation on the power grid planning scheme which is not in the set range to obtain a varied power grid planning scheme set under the current iteration times.
In practical application, the step of taking the power grid planning scheme with the minimum fitness in the current iteration times and the power grid planning scheme with the minimum fitness in the individual extreme value of the previous iteration times as the individual extreme value of the current iteration times specifically includes:
and under the k-th traversal, judging whether the fitness of the power grid planning scheme under the current traversal times in the power grid planning scheme set under the current iteration times is smaller than the fitness of the individual extreme value of the previous iteration times, and obtaining a third judgment result.
If the third judgment result is negative, determining that the individual extreme value of the previous iteration number is a candidate scheme under the current traversal number; and if the third judgment result is yes, determining the power grid planning scheme under the current traversal times as a candidate scheme under the current traversal times.
And judging whether the power grid planning scheme in the power grid planning scheme set under the current iteration number is traversed or not to obtain a fourth judgment result.
And if the fourth judgment result is negative, updating the traversal times and entering the next traversal.
And if the fourth judgment result is yes, determining that the scheme to be selected under the current traversal times is the individual extreme value of the current iteration times.
In practical application, the load flow calculation of the power distribution network comprising the distributed power supply specifically comprises the following steps:
as shown in fig. 2, the distributed power sources are regarded as nodes with constant power factors in the embodiment, and the power flow calculation of the power distribution network including the distributed power sources is described in detail below.
The introduction of the distributed power supply may change the power flow P of the line ij +jQ ij In this way, adverse effects on the network and relay protection are generated, so that the phenomenon of power flow backflow should be eliminated. The best way to deal with this phenomenon is to limit the capacity of the distributed power supply and let P DG,i ≤P Load,i . In this case, part of the power of the load is supplied by the distributed power supply and another part of the power is absorbed from the line, and the power absorbed by the load from the line is reduced compared to a network without the distributed power supply, thereby reducing the active loss on the line.
A forward-backward substitution method is adopted for power flow calculation of a power distribution network containing a distributed power supply, and then a power flow calculation equation on a line can be expressed as follows:
Figure BDA0003141477900000161
Figure BDA0003141477900000162
Figure BDA0003141477900000163
wherein, a ij Is used for indicating whether nodes i and j are connected or not, and a is when the nodes i and j are connected ij =1, otherwise a ij ＝0。
As shown in fig. 3, for the local power flow of the radial network, the power flow calculation formula on each line is:
Figure BDA0003141477900000164
as shown in fig. 4, the present embodiment provides specific steps of solving a power grid planning model by using a particle swarm algorithm:
the planning of the embodiment is to perform location and volume planning on the distributed power supply under the determined grid structure, and according to the characteristics of the planning problem, the following integer coding mode is adopted. N nodes in the planning system can be used for installing wind power and photovoltaic distributed power supplies, the installation scheme of the coded distributed power supplies is represented by a group of variables, and X = { X = DWG,1 ,x DWG,2 ,…,x DWG,N ,x PV,1 ,x PV,2 ,…,x PV,N }，S 0 The unit capacity is the distributed power supply installation capacity of the node i is S DG,i ＝S DWG,i +S PV,i ＝x DWG,i S 0 +x PV,i S 0 Wherein x is DWG,i ,x PV,i ∈[0,x i,max ]，x DWG,N Of wind-electric generators representing the Nth nodeOutput power, x PV,N Represents the output power of the photovoltaic generator at the Nth node when x DWG,i =0 or x PV,i And when the number is not less than 0, the node is not provided with a fan or a photovoltaic.
(1) And inputting data of the power distribution network to be planned, wherein the data comprises a grid structure, impedance information of each branch and node load information. And setting model parameters including an upper limit of a system voltage amplitude, an upper limit of installation capacity of the distributed power supply, rated power of electric heating and the like. Setting algorithm parameters including a population scale M, a maximum iteration number Max and an inertia weight parameter w max 、w min Velocity update parameter c 1 、c 2 Individual speed and position upper and lower limits, etc.
(2) Initializing a population, and randomly generating a population (an initial power grid planning scheme set) X = { X ] in a feasible domain 1 ,…,X M The position of each particle (initial grid planning scheme) (k =1,2, …, M) is
Figure BDA0003141477900000171
The iterative velocity of the particle is V k (iter)，
Figure BDA0003141477900000172
Are all 2N dimensional variables; the current number of iterations is 0 and the number of iterations is iter =0.
(3) According to the flow of load flow calculation and the randomly generated distributed power supply access condition (initial population), load flow calculation is carried out on the power distribution network to obtain the load flow of each node under the power distribution network corresponding to each particle
Figure BDA0003141477900000173
And all the groups are M.
(4) Calculating the fitness value f of each particle, and finding out the individual extreme value P corresponding to the minimum fitness value according to the initial particle fitness value best And group extremum G best . The individual extreme value is the internal search of the population of each iteration, the population extreme value is the global search containing all iterations, and the position X of the responding particle is recorded k 。P best And G best Are all vectors with dimension 2N, respectivelyCorresponding fitness value f (P) best )、f(G best )。
(5) The number of iterations iter = iter +1. The inertial weight is updated and the inertial weight is updated,
Figure BDA0003141477900000174
the inertial weight w keeps the particles inertial in motion, making them prone to expand the search space with the ability to explore new regions. If w is larger, the particles have the ability to expand the search space and search the area which is not reached before, and the overall search ability of the whole algorithm is strong; if w is smaller, the particle needs to search near the current solution, and the local search capability is stronger. Therefore, the adaptive adjustment strategy is adopted, so that w is linearly reduced along with the iteration of the algorithm, and the convergence performance of the algorithm is obviously improved.
(6) Particle number k =1. Updating particle velocity and position vectors
Figure BDA0003141477900000181
Figure BDA0003141477900000182
Wherein k =1,2,3, …, M, represents each particle in the population; rand () is between [0,1]A random number in between, and a random number,
Figure BDA0003141477900000183
and (3) representing the particle updating speed of the kth power grid planning scheme in the power grid planning scheme set under iter +1 iteration times.
(7) For the
Figure BDA0003141477900000184
Element of (2) { x } k,DWG,1 ,x k,DWG,2 ,…,x k,DWG,N ,x k,PV,1 ,x k,PV,2 ,…,x k,PV,N And checking whether each element flies out of a feasible field, and if so, performing boundary mutation operation on the elements of the particle vector.
Figure BDA0003141477900000185
In the above formula, X represents X k (iter+1)＝{x k,DWG,1 ,x k,DWG,2 ,…,x k,DWG,N ,x k,PV,1 ,x k,PV,2 ,…,x k,PV,N Each element in (b). Take a =0.25. If each element is in the feasible domain, i.e. x belongs to [ x ] min ,x max ]Then (8) is directly performed.
(8) For new particle
Figure BDA0003141477900000186
The power distribution network which participates in the composition carries out load flow calculation again, and the fitness of each particle in the new population is calculated
Figure BDA0003141477900000187
(9) Comparing the current individual extreme value P best Corresponding fitness value f (P) best ) Fitness value from a new iteration. If it is
Figure BDA0003141477900000188
Then turn to (10), otherwise turn to (13).
(10) Updating individual extrema
Figure BDA0003141477900000189
And corresponding fitness value
Figure BDA00031414779000001810
And turns to (13).
(11) Comparison
Figure BDA00031414779000001811
Fitness f (G) corresponding to current population extremum best ) If, if
Figure BDA00031414779000001812
And (12) turning, otherwise, turning (13).
(12) Updating group extrema
Figure BDA00031414779000001813
Updating the corresponding fitness simultaneously
Figure BDA00031414779000001814
And turns to (13).
(13) Judging whether all particles in the population are traversed or not, if k is less than M, k = k +1, and turning to (6); if k = M steering (14)
(14) Judging iter < iter Max If yes, turning to the step (5) and carrying out the next iteration cycle; otherwise, the iteration is ended, and the process is turned to (15).
(15) The capacity S of each node wind power supply access can be obtained according to the group extreme value when the optimal planning scheme (group extreme value) is output DWG,i Capacity S corresponding to photovoltaic power supply PV,i 。
The present embodiment provides a power distribution network planning system corresponding to the power distribution network planning method, as shown in fig. 5, the system includes:
the acquisition module A1 is used for acquiring model parameters in a power grid; the model parameters comprise active output power of the distributed power supply of each node, output power of the photovoltaic generator of each node, output power of the wind driven generator of each node, electric power of the direct heater and electric power of the heat storage heater.
And the model construction module A2 is used for constructing a power grid planning model based on the model parameters.
The scheme determining module A3 is used for solving the power grid planning model by adopting a particle swarm algorithm to obtain an optimal power grid planning scheme; the planning scheme comprises the output power of the photovoltaic generator and the output power of the wind driven generator of each node; the sum of the parameters corresponding to the optimal power grid planning scheme is minimum; the sum of the parameters is the sum of network loss, voltage stability and voltage deviation.
As an optional implementation, the plan planning module includes:
the updating unit is used for obtaining a power grid planning scheme set under the current iteration times according to the parameter set under the previous iteration times and the inertia weight under the current iteration times under the nth iteration times; the parameter set comprises a particle updating speed, a power grid planning scheme set, an individual extremum and a group extremum.
And the load flow calculation unit is used for obtaining the voltage amplitude, the inflow active power and the inflow reactive power of each node in each power grid planning scheme in the power grid planning scheme set under the current iteration times according to the power grid planning scheme set and the load flow calculation formula under the current iteration times.
And the fitness calculation unit is used for inputting the voltage amplitude of each node, the inflow active power and the inflow reactive power into the power grid planning model to obtain the fitness of each power grid planning scheme in the power grid planning scheme set under the current iteration number.
And the individual extreme value determining unit is used for taking the power grid planning scheme with the minimum fitness under the current iteration times and the power grid planning scheme with the minimum fitness in the individual extreme value of the last iteration times as the individual extreme value of the current iteration times.
And the group extreme value determining unit is used for taking the power grid planning scheme with the minimum fitness in the group extreme values of the previous iteration times and the power grid planning scheme with the minimum fitness in the group extreme values of the previous iteration times as the group extreme values of the current iteration times.
And the first judgment unit is used for judging whether the current iteration times reach the set iteration times to obtain a first judgment result.
And the scheme determining unit is used for determining the group extreme value under the current iteration number as the optimal power grid planning scheme if the first judgment result is yes.
And the updating iteration unit is used for entering the next iteration and updating the inertia weight if the first judgment result is negative.
As an optional implementation, the solution planning module further includes:
and the second judgment unit is used for judging whether the power grid planning scheme in the power grid planning scheme set under the current iteration times is not in the set range or not to obtain a second judgment result.
And if the second judgment result is yes, performing variation operation on the power grid planning scheme which is not in the set range to obtain a varied power grid planning scheme set under the current iteration number.
As an optional implementation, the individual extremum determining unit includes:
and the third judging subunit is configured to, in the kth traversal, judge whether the fitness in the power grid planning scheme of the current traversal number in the power grid planning scheme set of the current iteration number is smaller than the fitness of the individual extreme value of the previous iteration number, and obtain a third judgment result.
A candidate scheme determining subunit, configured to determine, if the third determination result is negative, that the individual extremum of the previous iteration number is the candidate scheme in the current traversal number; and if the third judgment result is yes, determining the power grid planning scheme under the current traversal times as a candidate scheme under the current traversal times.
And the fourth judging subunit is used for judging whether the power grid planning scheme in the power grid planning scheme set under the current iteration number is traversed or not to obtain a fourth judging result.
And the next traversal subunit is used for updating the traversal times and entering the next traversal if the fourth judgment result is negative.
And the individual extreme value determining subunit is configured to determine that the to-be-selected scheme under the current traversal number is the individual extreme value of the current iteration number if the fourth determination result is yes.
The invention has the following technical effects:
1. according to the invention, a power grid planning model considering distributed power sources and electric heating is established, so that the planning scheme can better adapt to the influence of the access of the electric heating on the power grid, and the power supply reliability and the electric energy quality of the large-scale access of the electric heating to the power distribution network are effectively ensured.
2. The improved particle swarm optimization is adopted to solve the planning model, the capability of the algorithm for solving high-dimensional and complex problems is enhanced, and the global search and the local search can be better considered.
In the present specification, the embodiments are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (9)

1. A power distribution network planning method is characterized by comprising the following steps:
obtaining model parameters in a power grid; the model parameters comprise active output power of the distributed power supply of each node, output power of the photovoltaic generator of each node, output power of the wind driven generator of each node, electric power of the direct heater and electric power of the heat storage heater;
constructing a power grid planning model based on the model parameters;
solving the power grid planning model by adopting a particle swarm algorithm to obtain an optimal power grid planning scheme; the planning scheme comprises the output power of the photovoltaic generator and the output power of the wind driven generator of each node; the sum of the parameters corresponding to the optimal power grid planning scheme is minimum; the parameter sum is the sum of network loss, voltage stability and voltage deviation;
the power grid planning model is as follows:
Figure FDA0003945883980000011
Figure FDA0003945883980000012
Figure FDA0003945883980000013
Figure FDA0003945883980000014
Figure FDA0003945883980000015
U i,min ≤U i ≤U i,max
x DWG,i S 0 +x PV,i S 0 ≤S DG,i,max
0＜P d ≤P h
0＜P c ≤P h wherein f is fitness, N is the total number of nodes of the power distribution network, and P is ij Representing the active power, Q, flowing through the end of branch ij ij Representing the reactive power, U, flowing through the ends of the branches ij j Representing the voltage magnitude of node j, R ij Resistance, k, representing branch ij ij Represents the state quantity, m, of branch ij ij Indicating the voltage stabilization of branch ij, U i Representing the magnitude of the voltage at node i, U i,N Indicating the nominal voltage, P, of node i j Representing the active power, X, flowing into node j ij Representing the reactance, Q, of branch ij j Representing reactive power, P, flowing into node i i Representing the active power, P, flowing into node i DG,i Representing the active output power, P, of the distributed power supply of node i Load,i Representing the total equivalent active load of node i, G ij Denotes the conductance, delta, of branch ij ij Representing the difference between the phase angles of the voltages at node i and node j, S ij,min Representing the lower limit, S, of the apparent power flowing on branch ij ij,max Indicating upstream of branch ijUpper limit of the apparent power, U, passed i,min Represents the lower limit, U, of the voltage amplitude of node i i,max Represents the upper limit, x, of the voltage magnitude of node i DWG,i Output power of the wind turbine, S, representing node i 0 Denotes unit capacity, x PV,i Output power of the photovoltaic generator, S, representing node i DG,i,max Represents the upper limit, P, of the installation capacity of the distributed power supply at the ith node d Representing the electric power of the direct heater, P h Indicating the rated power, P, of the electric heating c Represents the electric power of the heat-storage heater,
Figure FDA0003945883980000021
representing the power factor of the load, B ij The susceptance of the branch ij is represented,
Figure FDA0003945883980000022
representing the power factor.
2. The power distribution network planning method according to claim 1, wherein the solving of the power distribution network planning model by using the particle swarm optimization to obtain an optimal power distribution network planning scheme specifically comprises:
under the nth iteration number, obtaining a power grid planning scheme set under the current iteration number according to the parameter set under the last iteration number and the inertia weight under the current iteration number; the parameter set comprises a particle updating speed, a power grid planning scheme set, an individual extreme value and a group extreme value;
obtaining the voltage amplitude, the inflow active power and the inflow reactive power of each node in each power grid planning scheme in the power grid planning scheme set under the current iteration times according to the power grid planning scheme set and the load flow calculation formula under the current iteration times;
inputting the voltage amplitude of each node, the inflow active power and the inflow reactive power into the power grid planning model to obtain the fitness of each power grid planning scheme in the power grid planning scheme set under the current iteration times;
taking the power grid planning scheme with the minimum fitness in the current iteration times and the power grid planning scheme with the minimum fitness in the individual extreme values of the previous iteration times as the individual extreme values of the current iteration times;
taking the power grid planning scheme with the minimum fitness in the previous n iterations and the power grid planning scheme with the minimum fitness in the population extreme value of the previous iteration as the population extreme value of the current iteration;
judging whether the current iteration times reach set iteration times or not to obtain a first judgment result;
if the first judgment result is yes, determining the group extreme value under the current iteration number as an optimal power grid planning scheme;
and if the first judgment result is negative, entering the next iteration and updating the inertia weight.
3. The power distribution network planning method according to claim 2, wherein the power grid planning scheme set in the current iteration number is obtained according to the parameter set in the previous iteration number and the inertia weight in the current iteration number, and specifically comprises:
according to the formula
Figure FDA0003945883980000041
Obtaining a power grid planning scheme set under the current iteration times, wherein,
Figure FDA0003945883980000042
represents the kth power grid planning scheme in the power grid planning scheme set under iter +1 iteration times,
Figure FDA0003945883980000043
represents the kth power grid planning scheme in the power grid planning scheme set under iter iteration times, w represents the inertia weight under iter +1 iteration times,
Figure FDA0003945883980000044
representing the particle update speed of the kth grid planning scheme in the set of grid planning schemes under iter iteration times, c 1 Representing individual extremum coefficients, rand () representing a random function, P best Representing individual extrema at iter iterations, c 2 Representing the coefficient of the extremum of the population, G best Representing the population extremum at iter iterations.
4. The power distribution network planning method according to claim 2, wherein before obtaining the voltage amplitude, the incoming active power and the incoming reactive power of each node in each power grid planning scheme in the power grid planning scheme set at the current iteration number according to the power grid planning scheme set at the current iteration number and the power flow calculation formula, the method specifically comprises:
judging whether a power grid planning scheme in the power grid planning scheme set under the current iteration times is not in a set range or not to obtain a second judgment result;
and if the second judgment result is yes, performing variation operation on the power grid planning scheme which is not in the set range to obtain a varied power grid planning scheme set under the current iteration times.
5. The power distribution network planning method according to claim 2, wherein the power grid planning scheme with the minimum fitness in the current iteration number and the power grid planning scheme with the minimum fitness in the individual extreme value of the previous iteration number are used as the individual extreme value of the current iteration number, and specifically includes:
under the k-th traversal, judging whether the fitness in the power grid planning scheme under the current traversal times in the power grid planning scheme set under the current iteration times is smaller than the fitness of the individual extreme value of the previous iteration times to obtain a third judgment result;
if the third judgment result is negative, determining that the individual extreme value of the previous iteration number is a candidate scheme under the current traversal number; if the third judgment result is yes, determining the power grid planning scheme under the current traversal times as a candidate scheme under the current traversal times;
judging whether the power grid planning scheme in the power grid planning scheme set under the current iteration number is traversed or not to obtain a fourth judgment result;
if the fourth judgment result is negative, updating the traversal times and entering next traversal;
and if the fourth judgment result is yes, determining that the scheme to be selected under the current traversal times is the individual extreme value of the current iteration times.
6. A power distribution network planning system, comprising:
the acquisition module is used for acquiring model parameters in the power grid; the model parameters comprise active output power of the distributed power supply of each node, output power of the photovoltaic generator of each node, output power of the wind driven generator of each node, electric power of the direct heater and electric power of the heat storage heater;
the model construction module is used for constructing a power grid planning model based on the model parameters;
the scheme determining module is used for solving the power grid planning model by adopting a particle swarm algorithm to obtain an optimal power grid planning scheme; the planning scheme comprises the output power of the photovoltaic generator of each node and the output power of the wind driven generator; the sum of the parameters corresponding to the optimal power grid planning scheme is minimum; the parameter sum is the sum of network loss, voltage stability and voltage deviation;
the power grid planning model is as follows:
Figure FDA0003945883980000061
Figure FDA0003945883980000062
Figure FDA0003945883980000063
Figure FDA0003945883980000064
Figure FDA0003945883980000065
U i,min ≤U i ≤U i,max
x DWG,i S 0 +x PV,i S 0 ≤S DG,i,max
0＜P d ≤P h
0＜P c ≤P h wherein f is fitness, N is the total number of nodes of the power distribution network, and P is ij Representing the active power, Q, flowing through the end of branch ij ij Representing the reactive power, U, flowing through the ends of the branches ij j Representing the voltage magnitude of node j, R ij Resistance, k, representing branch ij ij Represents the state quantity, m, of branch ij ij Indicating the voltage stabilization of branch ij, U i Representing the voltage magnitude, U, of node i i,N Indicating the nominal voltage, P, of node i j Representing the active power, X, flowing into node j ij Representing the reactance, Q, of branch ij j Representing reactive power, P, flowing into node i i Representing the active power, P, flowing into node i DG,i Representing the active output power, P, of the distributed power supply of node i Load,i Representing the total equivalent active load of node i, G ij Denotes the conductance, delta, of branch ij ij Representing the difference between the phase angles of the voltages at node i and node j, S ij,min Representing the lower limit, S, of the apparent power flowing on branch ij ij,max Representing the upper limit, U, of the apparent power flowing on branch ij i,min Represents the lower limit, U, of the voltage amplitude of node i i,max Representing the upper limit, x, of the voltage magnitude of node i DWG,i Output power of a wind turbine representing node i, S 0 The amount of the carbon dioxide is expressed in terms of unit capacity,x PV,i output power of the photovoltaic generator, S, representing node i DG,i,max Represents an upper bound, P, of the distributed power source installation capacity at the ith node d Representing electric power, P, of the direct heater h Indicating the rated power, P, of the electric heating c Indicates the electric power of the heat-storage heater,
Figure FDA0003945883980000071
representing the power factor of the load, B ij The susceptance of the branch ij is represented,
Figure FDA0003945883980000072
representing the power factor.
7. The power distribution network planning system of claim 6, wherein the scheme determination module comprises:
the updating unit is used for obtaining a power grid planning scheme set under the current iteration times according to the parameter set under the last iteration times and the inertia weight under the current iteration times under the nth iteration times; the parameter set comprises a particle updating speed, a power grid planning scheme set, an individual extremum and a group extremum;
the load flow calculation unit is used for obtaining the voltage amplitude, the inflow active power and the inflow reactive power of each node in each power grid planning scheme in the power grid planning scheme set under the current iteration times according to the power grid planning scheme set and the load flow calculation formula under the current iteration times;
the fitness calculation unit is used for inputting the voltage amplitude of each node, the inflow active power and the inflow reactive power into the power grid planning model to obtain the fitness of each power grid planning scheme in the power grid planning scheme set under the current iteration times;
the individual extreme value determining unit is used for taking the power grid planning scheme with the minimum fitness under the current iteration times and the power grid planning scheme with the minimum fitness in the individual extreme values of the previous iteration times as the individual extreme values of the current iteration times;
the group extreme value determining unit is used for taking the power grid planning scheme with the minimum fitness under the previous n times of iteration times and the power grid planning scheme with the minimum fitness in the group extreme value of the previous iteration times as the group extreme value of the current iteration times;
the first judgment unit is used for judging whether the current iteration times reach the set iteration times or not to obtain a first judgment result;
the scheme determining unit is used for determining the group extreme value under the current iteration number as the optimal power grid planning scheme if the first judgment result is yes;
and the updating iteration unit is used for entering the next iteration and updating the inertia weight if the first judgment result is negative.
8. The power distribution network planning system of claim 7, wherein the plan determination module further comprises:
the second judgment unit is used for judging whether the power grid planning scheme in the power grid planning scheme set under the current iteration times is not in the set range or not to obtain a second judgment result;
and if the second judgment result is yes, performing variation operation on the power grid planning scheme which is not in the set range to obtain a varied power grid planning scheme set under the current iteration times.
9. The system according to claim 7, wherein the individual extremum determining unit comprises:
the third judging subunit is configured to, in a kth traversal, judge whether the fitness in the power grid planning scheme in the current traversal number of times in the power grid planning scheme set in the current iteration number is smaller than the fitness of the individual extreme value of the previous iteration number, and obtain a third judgment result;
a candidate scheme determining subunit, configured to determine, if the third determination result is negative, that the individual extremum of the previous iteration number is the candidate scheme in the current traversal number; if the third judgment result is yes, determining the power grid planning scheme under the current traversal times as a candidate scheme under the current traversal times;
the fourth judging subunit is configured to judge whether the power grid planning scheme in the power grid planning scheme set under the current iteration number is completely traversed to obtain a fourth judging result;
the next traversal subunit is used for updating the traversal times and entering next traversal if the fourth judgment result is negative;
and the individual extreme value determining subunit is configured to determine that the to-be-selected scheme under the current traversal number is the individual extreme value of the current iteration number if the fourth determination result is yes.
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