CN110909939A - Multi-stage planning method for power distribution network with distributed power supplies - Google Patents

Multi-stage planning method for power distribution network with distributed power supplies Download PDF

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CN110909939A
CN110909939A CN201911154872.2A CN201911154872A CN110909939A CN 110909939 A CN110909939 A CN 110909939A CN 201911154872 A CN201911154872 A CN 201911154872A CN 110909939 A CN110909939 A CN 110909939A
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李新
魏俊
叶圣永
刘旭娜
张文涛
韩宇奇
赵达维
李达
龙川
刘洁颖
张玉鸿
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Abstract

The invention discloses a multi-stage planning method for a power distribution network containing distributed power supplies, which comprises the following steps: s1: acquiring load prediction data of a power distribution network containing a distributed power supply, carrying out k-means clustering on the medium and long-term load prediction data of the power distribution network according to the acceleration of the load level, and taking a clustering result as a multi-stage division basis to obtain the division years of each stage; s2: constructing a planning model by taking the minimum current total cost value in a planning period of the power distribution network as an optimization target and taking a DG power flow equation, a node voltage opportunity, a DG installation capacity of a node to be selected and a DG planning total capacity as constraint conditions; s3: and solving the planning model by utilizing a particle swarm algorithm of the contraction factor to obtain the DG access position and capacity configuration. Compared with the traditional planning method, the planning method provided by the invention has the advantages of lower economic cost and higher distributed power supply permeability.

Description

Multi-stage planning method for power distribution network with distributed power supplies
Technical Field
The invention relates to the field of power distribution network planning of a power system, in particular to a multi-stage planning method for a power distribution network with distributed power supplies.
Background
With the development of clean energy, the access of a large number of distributed power sources dg (distributed generation) brings great influence to the planning and operation of a power distribution network, and the difficulty of power supply planning is improved. Considering the overall layout of the long-term planning of the power distribution network, the research of the multi-stage planning method becomes a hotspot. The pseudo dynamic programming method, the mixed integer linear programming method and the like are applied to multi-stage programming of the power distribution network, and some research results are obtained. However, few documents consider multi-stage division methods, and often perform equal-year division according to experience, and a planning and standard model is lacked, so that the practical value of a planning scheme is not high.
Disclosure of Invention
The invention aims to provide a multi-stage planning method for a power distribution network containing a distributed power supply, which is based on a k-means clustering technology, realizes multi-stage location and volume fixing of the distributed power supply, and improves the economical efficiency and the practicability of a planning scheme under a traditional multi-stage model.
The invention is realized by the following technical scheme:
a multi-phase planning method for a power distribution network with distributed power supplies comprises the following steps:
s1: acquiring load prediction data of a power distribution network containing a distributed power supply, carrying out k-means clustering on the medium-term and long-term load prediction data of the power distribution network according to the acceleration of the load level, and taking a clustering result as a multi-stage division basis to obtain the division years of each stage;
s2: constructing a planning model by taking the minimum current total cost value in a planning period of the power distribution network as an optimization target and taking a DG power flow equation, a node voltage opportunity, a DG installation capacity of a node to be selected and a DG planning total capacity as constraint conditions;
s3: and solving the planning model by utilizing a particle swarm algorithm of the contraction factor to obtain the DG access position and capacity configuration.
The DG referred to in this invention is a distributed power supply.
The planning period is divided into a plurality of stages, and the medium-long term load prediction data is subjected to scene clustering by adopting a K-means clustering method to obtain the planning period of each stage. And calculating the cost of each stage, and summing to obtain the cost of the whole planning period, wherein the cost is used as a planning objective function. And taking a power flow equation as an equality constraint, taking the node voltage as an opportunity constraint and a distributed power supply installation capacity constraint of each node. And solving by adopting a shrinkage factor particle swarm algorithm to obtain the final DG access position and capacity configuration. Compared with the traditional planning method, the planning method provided by the invention has the advantages of lower economic cost and higher distributed power supply permeability.
Further, the total current value in step S2 includes DG construction costs, DG operation maintenance costs and costs for purchasing electricity from the distribution grid to the upper level grid.
Further, the function expression of the planning model is as follows:
Figure BDA0002284532170000021
in the formula, muyuIn order to obtain the current value coefficient,
Figure BDA0002284532170000022
and respectively charging the DG construction cost, the DG operation maintenance cost and the power purchasing cost of the power distribution network to a superior power grid in each stage.
Further, the current value coefficient reduces the annual average cost of each stage to the current muyuThe expression of (a) is as follows:
μyu＝(1+γ)-[(u-1)Y+y]
in the formula: u represents the current stage, Y represents the number of years that the stage has gone through, and Y represents the total number of years per stage.
Further, the function expression of the DG construction costs is as follows:
Figure BDA0002284532170000023
Figure BDA00022845321700000210
in the formula:
Figure BDA0002284532170000024
respectively representing the investment cost of the unit capacity of the fan and the photovoltaic power supply in the ith node of the u stage;
Figure BDA0002284532170000025
respectively representing rated installation capacities of the ith node fan and the photovoltaic power supply in the u stage; n is a radical ofIDGAnd installing the number of nodes to be selected for the IDG. Tau is the capital recovery factor for the equal share of construction cost to life each year, gamma is the inflation rate, LTThe equipment life.
Further, the function expression of the DG operating and maintenance costs is as follows:
Figure BDA0002284532170000026
in the formula:
Figure BDA0002284532170000027
respectively show the installation of the u-th stage at the i-th sectionOperating and maintaining costs of unit generated energy of the point central fan and the photovoltaic power supply respectively;
Figure BDA0002284532170000028
respectively representing the annual energy production of a fan and a photovoltaic arranged at the ith node in the u stage; cP,uRepresenting the average electricity selling price of the u stage; ploss,uAnd the network loss of the distribution network after the DG is accessed in the u stage.
Further, the function expression of the power purchase cost of the power distribution network to the superior power grid is as follows:
Figure RE-GDA00023380173000000210
in the formula: rhouThe unit electricity purchasing cost of purchasing electricity from the power distribution network to the superior power grid in the u stage is represented; egrid,uAnd purchasing electric quantity to the upper-level power grid for the u-stage power distribution network.
Further, the functional expression of the DG power flow equation in step S2 is as follows:
Figure BDA0002284532170000031
in the formula: pi,u、Qi,uRespectively the active power injection quantity and the reactive power injection quantity of the node i; u shapei,u、Uj,uRespectively, voltage amplitudes; deltaijIs the voltage phase angle difference; gij、BijRespectively the real part and the imaginary part of the admittance matrix; n is the number of system nodes;
the functional expression of the node voltage opportunity is as follows:
Figure BDA0002284532170000032
the above equation indicates that the system is at voltage confidence level βUVoltage constraint of Ui,uRepresents the voltage of the node i in the u stage;
Figure BDA0002284532170000033
and
Figure BDA0002284532170000034
respectively representing the minimum value and the maximum value of the voltage allowed by the node i;
the function expression of the installation capacity of the node DG to be selected is as follows:
Figure BDA0002284532170000035
in the formula:
Figure BDA0002284532170000036
setting a DG capacity upper limit allowed to be installed for a node i to be selected;
the function expression of DG planned total capacity is as follows:
∑PDG,u≤η∑Pload,u
in the formula: pDG,uInstalling Total Capacity for Uth stage distributed Power supply, Pload,uFor the u-th stage system total load capacity, η is the maximum penetration rate of the distributed power supply allowed by the system.
Further, the implementation process of S3 is as follows:
the method comprises the following steps of taking the installation number of WTG (point-to-point) and PVG (point-to-point) nodes to be selected as control variables, and solving a multi-stage planning model of the distributed power supply by adopting CFPSO (computational fluid power system), wherein the coding mode of the control variables is as follows:
Figure BDA0002284532170000037
Figure BDA0002284532170000038
in the formula: xWTGAnd XPVGIn (1)
Figure BDA0002284532170000041
Respectively representing the distributed wind power and distributed optical volt-ampere loading number of each node to be selected in the u stage;
for a certain node to be selected, the DG installation number in the u stageCannot be less than the u-1 th stage, then XWTGAnd XPVGThe elements in (A) need to satisfy:
x1,i≤x2,i≤…xu,i，i＝1,2,…,NIDG。
further, the solving step of the multi-stage planning model is as follows:
s31: inputting power distribution network parameters, DG parameters and CFPSO parameters;
s32: randomly generating an initial population by the coding form of the control variable;
s33: performing probability load flow calculation on all individuals, and judging whether constraint conditions are violated or not;
s34: calculating an objective function value of a planning scheme corresponding to each individual;
s35: calculating all individual fitness values, and reducing the individual fitness values violating the constraint condition by adopting a penalty function method;
s36: updating the speed and position of the particles;
s37: calculating a particle adaptation value;
s38: and when the CFPSO algorithm reaches the maximum iteration times, stopping searching and outputting an optimization result, or returning to S36 to continue searching.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention discloses a multi-stage planning method for a power distribution network with distributed power supplies, which takes the problem of stage division in the planning of medium and long-term power distribution networks into consideration, and adopts a k-means clustering method to perform clustering analysis on medium and long-term load prediction data to obtain a multi-stage division scheme for the power distribution network. And establishing a power distribution network multi-stage power supply planning model by taking the minimum current total cost value in a planning period as an optimization target, and solving by adopting a shrinkage factor particle swarm algorithm to obtain a distributed power supply multi-stage planning scheme.
2. According to the multi-stage planning method for the power distribution network with the distributed power supply, disclosed by the invention, the actual growth rate of the load and the stage of power supply construction are considered at the same time, so that a more economic planning scheme is obtained, and the permeability of the distributed power supply is improved.
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The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a flow chart of a distributed power multi-phase planning method;
FIG. 2 is a flow chart of the CFPSO optimization algorithm of the present invention;
fig. 3 is a diagram of an initial IEEE33 network architecture;
FIG. 4 is a graph showing the effect of the multi-stage partitioning method for medium and long term loads based on k-means clustering.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not used as limitations of the present invention.
Example (b):
as shown in fig. 1, a multi-phase planning method for a power distribution network including distributed power sources includes the following steps:
s1: acquiring load prediction data of a power distribution network containing a distributed power supply, carrying out k-means clustering on the medium-term and long-term load prediction data of the power distribution network according to the acceleration of the load level, and taking a clustering result as a multi-stage division basis to obtain the division years of each stage;
s2: constructing a planning model by taking the minimum current total cost value in a planning period of the power distribution network as an optimization target and taking a DG power flow equation, a node voltage opportunity, a DG installation capacity of a node to be selected and a DG planning total capacity as constraint conditions;
the current total cost value comprises DG construction cost, DG operation and maintenance cost and power purchase cost of a power distribution network to a superior power grid;
the function of the planning model is expressed as follows:
Figure BDA0002284532170000051
in the formula, muyuIn order to obtain the current value coefficient,
Figure BDA0002284532170000052
respectively charging DG construction cost, DG operation maintenance cost and power purchasing cost of the power distribution network to a superior power grid at each stage;
the current value coefficient reduces the annual average cost of each stage to the current value muyuThe expression of (a) is as follows:
μyu＝(1+γ)-[(u-1)Y+y]
in the formula: u represents the current stage, Y represents the number of years that the stage has gone through, and Y represents the total number of years per stage;
the function expression of the DG construction cost is as follows:
Figure BDA0002284532170000053
Figure BDA0002284532170000056
in the formula:
Figure BDA0002284532170000054
respectively representing the investment cost of the unit capacity of the fan and the photovoltaic power supply in the ith node of the u stage;
Figure BDA0002284532170000055
respectively representing rated installation capacities of the ith node fan and the photovoltaic power supply in the u stage; n is a radical ofIDGAnd installing the number of nodes to be selected for the IDG. Tau is the capital recovery factor for the equal share of construction cost to life each year, gamma is the inflation rate, LTIs the equipment life;
the function expression of the DG operating maintenance cost is as follows:
Figure BDA0002284532170000061
in the formula:
Figure BDA0002284532170000062
respectively representing the operation and maintenance costs of unit power generation amount of the fan and the photovoltaic power supply installed in the ith node in the u stage;
Figure BDA0002284532170000063
respectively representing the annual energy production of a fan and a photovoltaic arranged at the ith node in the u stage; cP,uRepresenting the average electricity selling price of the u stage; ploss,uThe network loss of the distribution network after the DG is accessed in the u stage;
the function expression of the power purchase cost of the power distribution network to the superior power grid is as follows:
Figure RE-GDA0002338017300000064
in the formula: rhouThe unit electricity purchasing cost of purchasing electricity from the power distribution network to the superior power grid in the u stage is represented; egrid,uThe electric quantity purchased to the upper-level power grid for the u-stage power distribution network;
the functional expression of the DG power flow equation is as follows:
Figure BDA0002284532170000065
in the formula: pi,u、Qi,uRespectively the active power injection quantity and the reactive power injection quantity of the node i; u shapei,u、Uj,uRespectively, voltage amplitudes; deltaijIs the voltage phase angle difference; gij、BijRespectively the real part and the imaginary part of the admittance matrix; n is the number of system nodes;
the functional expression of the node voltage opportunity is as follows:
Figure BDA0002284532170000066
the above equation indicates that the system is at voltage confidence level βUVoltage constraint of Ui,uRepresents the voltage of the node i in the u stage;
Figure BDA0002284532170000067
and
Figure BDA0002284532170000068
respectively representing the minimum value and the maximum value of the voltage allowed by the node i;
the function expression of the installation capacity of the node DG to be selected is as follows:
Figure BDA0002284532170000069
in the formula:
Figure BDA00022845321700000610
setting a DG capacity upper limit allowed to be installed for a node i to be selected;
the function expression of DG planned total capacity is as follows:
∑PDG,u≤η∑Pload,u
in the formula: pDG,uInstalling Total Capacity for Uth stage distributed Power supply, Pload,uFor the u stage system total load capacity, η is the maximum penetration rate of the distributed power supply allowed by the system;
s3: solving the planning model by utilizing a particle swarm algorithm of the contraction factor to obtain the DG access position and capacity configuration, wherein the specific implementation process is as follows:
the installation number of the WTG and PVG nodes to be selected is used as a control variable, and the coding mode is as follows:
Figure BDA0002284532170000071
Figure BDA0002284532170000072
in the formula: xWTGAnd XPVGIn (1)
Figure BDA0002284532170000073
And respectively representing the distributed wind power and the distributed optical volt-ampere loading number of each node to be selected in the u stage.
For a certain node to be selected, the installing quantity of DGs in the u stage cannot be less than that in the u-1 stage, and then XWTGAnd XPVGThe elements in (A) need to satisfy:
x1,i≤x2,i≤…xu,i，i＝1,2,…,NIDG；
as shown in fig. 2, the CFPSO is used to solve the multi-stage planning model of the distributed power supply, and the solving steps are as follows:
s31: inputting power distribution network parameters, DG parameters and CFPSO parameters;
s32: randomly generating an initial population by the coding form of the control variable;
s33: performing probability load flow calculation on all individuals, and judging whether constraint conditions are violated or not;
s34: calculating an objective function value of a planning scheme corresponding to each individual;
s35: calculating all individual fitness values, and reducing the individual fitness values violating the constraint condition by adopting a penalty function method;
s36: updating the speed and position of the particles;
s37: calculating a particle adaptation value;
s38: and when the CFPSO algorithm reaches the maximum iteration times, stopping searching and outputting an optimization result, or returning to S36 to continue searching.
The initial network structure is shown in fig. 3, the system voltage level is 12.66kV, the nodes to be selected of DGs are 7, 11, 15, 18, 29 and 32, the rated capacity of a single DG is 100kW, the allowable range of the DG upper limit of 1000 kW. node voltage for each node to be selected is 0.9-1.1 (per unit value), and the confidence level βU＝0.95。
The annual increase of electricity consumption in the whole society in 2016-2020, 2021-2025 and 2025-2030 is 6.1%, 3.1% and 0.9%. Parameters of CFPSO: maximum number of iterations 200, population size 50, shrinkage factor 0.729.
The planning parameters related to the objective function are as follows: the unit capacity investment cost of each WTG is $ 1500/Kw, and the operation maintenance cost of unit power generation amount is $ 0.03/(kW.h); the PVG has a cost of 1750 dollars/kW per unit volume and a cost of 0.04 dollars/(kWh.h) per unit amount of power generation. The upward electricity purchase fee of the power distribution network is 0.05 dollar/(kW & h), and the service life of the equipment is 20 years.
K-means clustering is carried out on load prediction data from 2016 to 2030 to obtain a clustering result, as shown in fig. 4, a planning period of 15 years is divided into three stages, wherein the first stage is 2016 to 2019, the second stage is 2020 to 2023, and the third stage is 2024 to 2030.
In accordance with the above staging scheme, table 1 gives a multi-stage optimal planning scheme. By way of comparison, table 1 gives the results of the planning under the conventional multi-phase method, as well as the results of the single-phase planning. The results show that the overall cost of the configuration scheme obtained under the multi-stage planning method proposed herein is lower than that of the conventional multi-stage method, and a large amount of commissioning work is mainly performed in the first two stages of the planning period, improving the economy in the overall planning period. The cost is reduced by 14% compared to a single stage plan and the distributed power penetration is improved, thereby illustrating the necessity of a multi-stage configuration.
TABLE 1 DG optimal planning scheme
Figure BDA0002284532170000081
Note: 11(6,0) indicates that node 11 has 6 WTGs installed, no PVGs installed, and so on.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (10)

1. A multi-phase planning method for a power distribution network with distributed power supplies is characterized by comprising the following steps:
s1: acquiring load prediction data of a power distribution network containing a distributed power supply, carrying out k-means clustering on the medium and long-term load prediction data of the power distribution network according to the acceleration of the load level, and taking a clustering result as a multi-stage division basis to obtain the division years of each stage;
s2: constructing a planning model by taking the minimum current total cost value in a planning period of the power distribution network as an optimization target and taking a DG power flow equation, a node voltage opportunity, a DG installation capacity of a node to be selected and a DG planning total capacity as constraint conditions;
s3: and solving the planning model by utilizing a particle swarm algorithm of the contraction factor to obtain the DG access position and capacity configuration.
2. The multi-phase planning method for the power distribution network with the distributed power supplies of claim 1, wherein the current total cost values in the step S2 include DG construction cost, DG operation maintenance cost and electricity purchasing cost of the power distribution network to a superior power grid.
3. The multi-phase planning method for the power distribution network with the distributed power supplies according to claim 2, wherein the function expression of the planning model is as follows:
Figure FDA0002284532160000011
in the formula, muyuIn order to obtain the current value coefficient,
Figure FDA0002284532160000012
and respectively charging the DG construction cost, the DG operation maintenance cost and the power purchasing cost of the power distribution network to a superior power grid in each stage.
4. The multi-stage planning method for power distribution network with distributed power supplies according to claim 3, wherein the current coefficient reduces the annual average cost of each stage to the current value of μyuThe expression of (a) is as follows:
μyu＝(1+γ)-[(u-1)Y+y]
in the formula: u represents the current stage, Y represents the number of years that the stage has gone through, and Y represents the total number of years per stage.
5. The method of claim 3, wherein the function of the DG construction cost is expressed as follows:
Figure FDA0002284532160000013
Figure FDA0002284532160000014
in the formula:
Figure FDA0002284532160000015
respectively representing the investment cost of the unit capacity of the fan and the photovoltaic power supply in the ith node of the u stage;
Figure FDA0002284532160000016
respectively representing rated installation capacities of the ith node fan and the photovoltaic power supply in the u stage; n is a radical ofIDGAnd installing the number of nodes to be selected for the IDG. Tau is the capital recovery factor for the equal share of construction cost to life each year, gamma is the inflation rate, LTThe equipment life.
6. The method of claim 3, wherein the function of the operating and maintenance costs of the DGs is expressed as follows:
Figure FDA0002284532160000021
in the formula:
Figure FDA0002284532160000022
respectively shows that the u stage is arranged in the ith node, and the fan and the photovoltaic power supply are dividedThe operating and maintaining cost of other unit generating capacity;
Figure FDA0002284532160000023
respectively representing the annual energy production of a fan and a photovoltaic arranged at the ith node in the u stage; cP,uRepresenting the average electricity selling price of the u stage; ploss,uAnd the network loss of the distribution network after the DG is accessed in the u stage.
7. The multi-stage planning method for the power distribution network with the distributed power supplies as claimed in claim 3, wherein the function expression of the power purchase cost of the power distribution network to the superior power grid is as follows:
Figure RE-FDA0002338017290000024
in the formula: rhouThe unit electricity purchasing cost of purchasing electricity from the power distribution network to the superior power grid in the u stage is represented; egrid,uAnd purchasing electric quantity from the power distribution network to the superior power grid for the u-stage power distribution network.
8. The multi-phase planning method for the power distribution network with the distributed power supplies according to claim 1, wherein the function expression of the DG power flow equation in the step S2 is as follows:
Figure FDA0002284532160000025
in the formula: pi,u、Qi,uRespectively the active power injection quantity and the reactive power injection quantity of the node i; u shapei,u、Uj,uRespectively, voltage amplitudes; deltaijIs the voltage phase angle difference; gij、BijRespectively the real part and the imaginary part of the admittance matrix; n is the number of system nodes;
the functional expression of the node voltage opportunity is as follows:
Figure FDA0002284532160000026
the above equation indicates that the system is at voltage confidence level βUVoltage constraint of Ui,uRepresents the voltage of the node i in the u stage;
Figure FDA0002284532160000027
and
Figure FDA0002284532160000028
respectively representing the minimum value and the maximum value of the voltage allowed by the node i;
the function expression of the installation capacity of the node DG to be selected is as follows:
Figure FDA0002284532160000029
in the formula: pi maxSetting a DG capacity upper limit allowed to be installed for a node i to be selected;
the function expression of DG planned total capacity is as follows:
∑PDG,u≤η∑Pload,u
in the formula: pDG,uInstalling Total Capacity for Uth stage distributed Power supply, Pload,uFor the u-th stage system total load capacity, η is the maximum penetration rate of the distributed power supply allowed by the system.
9. The multi-phase planning method for the power distribution network with the distributed power supplies according to claim 1, wherein the implementation process of S3 is as follows:
and solving the multi-stage planning model of the distributed power supply by using the CFPSO by taking the installation number of the WTG and the PVG of the nodes to be selected as control variables.
10. The multi-phase planning method for the power distribution network with the distributed power supplies according to claim 9, wherein the solving of the multi-phase planning model comprises the following steps:
s31: inputting power distribution network parameters, DG parameters and CFPSO parameters;
s32: randomly generating an initial population by the coding form of the control variable;
s33: performing probability load flow calculation on all individuals, and judging whether constraint conditions are violated or not;
s34: calculating an objective function value of a planning scheme corresponding to each individual;
s35: calculating all individual fitness values, and reducing the individual fitness values violating the constraint condition by adopting a penalty function method;
s36: updating the speed and position of the particles;
s37: calculating a particle adaptation value;
s38: and when the CFPSO algorithm reaches the maximum iteration times, stopping searching and outputting an optimization result, and otherwise, returning to S36 to continue searching.
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