CN109546683B - Distributed photovoltaic receiving capacity margin optimization method for power distribution network nodes - Google Patents

Distributed photovoltaic receiving capacity margin optimization method for power distribution network nodes Download PDF

Info

Publication number
CN109546683B
CN109546683B CN201811523562.9A CN201811523562A CN109546683B CN 109546683 B CN109546683 B CN 109546683B CN 201811523562 A CN201811523562 A CN 201811523562A CN 109546683 B CN109546683 B CN 109546683B
Authority
CN
China
Prior art keywords
node
margin
admission
distribution network
power
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201811523562.9A
Other languages
Chinese (zh)
Other versions
CN109546683A (en
Inventor
王建明
潘志新
王守相
王洪坤
蔡声霞
袁栋
吴楠
方鑫
朱卫平
袁晓冬
陈兵
史明明
孙健
朱振
徐立
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Tianjin Xianghe Electric Technology Co ltd
Tianjin University
State Grid Corp of China SGCC
State Grid Jiangsu Electric Power Co Ltd
Electric Power Research Institute of State Grid Jiangsu Electric Power Co Ltd
Original Assignee
Tianjin Xianghe Electric Technology Co ltd
Tianjin University
State Grid Corp of China SGCC
State Grid Jiangsu Electric Power Co Ltd
Electric Power Research Institute of State Grid Jiangsu Electric Power Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Tianjin Xianghe Electric Technology Co ltd, Tianjin University, State Grid Corp of China SGCC, State Grid Jiangsu Electric Power Co Ltd, Electric Power Research Institute of State Grid Jiangsu Electric Power Co Ltd filed Critical Tianjin Xianghe Electric Technology Co ltd
Priority to CN201811523562.9A priority Critical patent/CN109546683B/en
Publication of CN109546683A publication Critical patent/CN109546683A/en
Application granted granted Critical
Publication of CN109546683B publication Critical patent/CN109546683B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H02J3/383—
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Energy or water supply
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E40/00—Technologies for an efficient electrical power generation, transmission or distribution
    • Y02E40/70—Smart grids as climate change mitigation technology in the energy generation sector

Landscapes

  • Business, Economics & Management (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Human Resources & Organizations (AREA)
  • Strategic Management (AREA)
  • Theoretical Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Marketing (AREA)
  • General Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Tourism & Hospitality (AREA)
  • Quality & Reliability (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Development Economics (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Supply And Distribution Of Alternating Current (AREA)

Abstract

The invention discloses a distributed photovoltaic admission capacity margin optimization method for nodes of a power distribution network, which comprises the steps of calculating the distributed photovoltaic limit admission capacity of each node of the power distribution network according to initial data; utilizing an upper-layer optimization model to optimize the photovoltaic power of the access node, and transmitting an admission margin balance coefficient to a lower-layer model; transmitting the internet power of the node user to an upper layer model by using a lower layer optimization model; after the mutual iteration of the upper layer model and the lower layer model is finished, the final optimal receiving capacity of the pre-access distributed photovoltaic node is obtained; and calculating an acceptance margin evaluation index of the distributed photovoltaic nodes of the power distribution network. According to the invention, operators can know how much space of each node of the power distribution network can continuously accept the distributed photovoltaic in detail according to the condition of the node acceptance margin, and node distributed photovoltaic users can actively optimize the network access power, so that the pressure of the power distribution network is reduced, and meanwhile, the benefit is increased.

Description

Power distribution network node distributed photovoltaic receiving capacity margin optimization method
Technical Field
The invention relates to a method for optimizing the margin of the distributed photovoltaic admitting ability of a power distribution network node, and belongs to the technical field of the admitting ability of the power distribution network node to distributed photovoltaic.
Background
At present, distributed photovoltaic access influences node voltage and line power flow of a power distribution network, so that the power distribution network cannot receive the distributed photovoltaic without limit, and existing research mainly focuses on two aspects of evaluation, calculation and improvement of receiving capacity of the power distribution network for the distributed photovoltaic. The admission capacity of the distribution network node to the distributed photovoltaic is rarely evaluated, calculated and analyzed from the node perspective.
The nodes are important components in the power distribution network structure, and the operation management department not only pays attention to the overall admission capacity of the power distribution network, but also more urgently hopes to master the admission capacity of each node of the power distribution network and guide the capacity expansion transformation of the power distribution network and the ordered access of the distributed photovoltaic of node users. The admission capacity of the node can fully reflect the overall admission level of the power distribution network.
The invention provides an evaluation and optimization method for receiving distributed photovoltaic by a power distribution network node, which defines the ultimate receiving capacity and the optimal receiving capacity of the node, provides 3 areas of the receiving capacity of the power distribution network, provides 4 node receiving margin evaluation indexes from the perspective of receiving the distributed photovoltaic by the node, and constructs a double-layer optimization model with optimal economy and maximum receiving margin.
Disclosure of Invention
The purpose is as follows: in order to overcome the defects in the prior art, the invention provides a method for optimizing the margin of distributed photovoltaic acceptance capacity of nodes of a power distribution network.
The technical scheme is as follows: in order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a distributed photovoltaic admission capacity margin optimization method for a power distribution network node comprises the following steps:
the method comprises the following steps: inputting node power of a power distribution network, line impedance, accessed distributed photovoltaic nodes and power and initial data of a network structure;
step two: calculating the distributed photovoltaic limit admission capacity of each node of the power distribution network according to the initial data;
step three: utilizing an upper-layer optimization model, optimizing the photovoltaic power of the access node by using the optimization target with the minimum network loss, the minimum power purchase of a higher-level power grid and the maximum optimal admission margin, meeting constraint conditions, and transmitting an admission margin balance coefficient to a lower-layer model;
step four: utilizing a lower-layer optimization model, carrying out photovoltaic internet power optimization by meeting constraint conditions with the maximum benefit of a node user as an optimization target, and transmitting the internet power of the node user to an upper-layer model;
step five: after the mutual iteration of the upper layer model and the lower layer model is finished, the final optimal receiving capacity of the pre-access distributed photovoltaic node is obtained
Figure GDA0001970144200000021
Step six: calculating an evaluation index of the admission margin of the distributed photovoltaic nodes of the power distribution network, as shown in table 1:
Figure GDA0001970144200000022
table 1 shows the node admission margin evaluation index.
Preferably, the distributed photovoltaic limit admission capacity of each node of the power distribution network refers to that under the constraint of line current, the voltage of any node in the power grid reaches the maximum allowable operating voltage or the line current reaches the distributed photovoltaic capacity corresponding to the maximum allowable current through the continuous increase of the photovoltaic capacity of the access node on the basis that the structure, the load and the distributed power supply of the existing power distribution network are not changed.
Preferably, the upper layer optimization model is shown in formula (1):
Figure GDA0001970144200000031
in the formula: t is a scheduling period, NliThe number of branches of the power distribution network;
Figure GDA0001970144200000032
power is lost for the line;
Pps,tthe power is purchased for the superior power grid, N is the number of nodes of the power distribution grid,
Figure GDA0001970144200000033
optimal acceptance margin for distributed photovoltaic nodes;
the constraint conditions of the upper layer optimization model are safety constraint and admission margin constraint of the power distribution network, and the constraint conditions comprise: the method comprises the following steps of (1) power flow equation constraint, power balance constraint, node voltage constraint, branch current constraint and limit admission margin constraint;
the power flow equation constraint is shown in formula (2):
Figure GDA0001970144200000034
in the formula: pi,t、Qi,tRespectively injecting active power and reactive power into the node; vi,t、Vj,tNode voltages at nodes i and j, respectively; gij、BijConductance and susceptance between nodes i and j, respectively; thetaijIs the phase angle difference between nodes i and j;
the power balance constraint is shown in equation (3):
Figure GDA0001970144200000035
in the formula: n is a radical ofpvConnecting photovoltaic node numbers to the power distribution network; n is a radical ofliFor the number of lines in the distribution network, Nlo＝N-NpvThe number of nodes of the power distribution network which are not connected with the photovoltaic is counted;
Figure GDA0001970144200000036
transmitting the network access power of the distributed photovoltaic nodes to the optimization variables of the upper model for the lower model;
Figure GDA0001970144200000037
accessing photovoltaic node electricity purchasing power;
Figure GDA0001970144200000038
load power for the node;
the node voltage constraint is as shown in equation (4):
Vimin≤Vi,t≤Vimax (4)
in the formula: vimin，VimaxThe minimum value and the maximum value of the node voltage are respectively;
the branch current constraint is shown in equation (5):
Iimin≤Ii,t≤Iimax (5)
in the formula: i isimin，IimaxThe minimum value and the maximum value of the branch current are respectively;
the limit admission margin constraint is shown in equation (6):
Figure GDA0001970144200000041
in the formula:
Figure GDA0001970144200000042
is the node admission area index.
Preferably, the lower layer optimization model is shown in formula (7):
Figure GDA0001970144200000043
in the formula:
Figure GDA0001970144200000044
parameters for the upper model to pass to the lower model for the admission margin balance coefficient
Figure GDA0001970144200000045
When taking 1, when
Figure GDA0001970144200000046
When is, [0,1]Taking values;
Figure GDA0001970144200000047
the photovoltaic grid-connected electricity price is obtained;
Figure GDA0001970144200000048
subsidizing electricity prices for photovoltaics;
Figure GDA0001970144200000049
purchasing electricity price for the power grid;
Figure GDA00019701442000000410
photovoltaic output is taken as a node;
Figure GDA00019701442000000411
purchasing power for the node;
Figure GDA00019701442000000412
purchase cost for stored energy j;
Figure GDA00019701442000000413
for the jth energy storage charge-discharge cycleThe number of life times;
Figure GDA00019701442000000414
respectively storing energy, discharging power and charging power; alpha and beta are energy storage charging and discharging state mark parameters and are variables of 0 to 1, and alpha + beta is less than or equal to 1;
the constraints are as follows:
the power balance constraint is shown in equation (8):
Figure GDA00019701442000000415
the node network access power constraint is shown as equation (9):
Figure GDA00019701442000000416
wherein,
Figure GDA00019701442000000417
limiting the acceptance capacity for the distributed photovoltaic nodes;
the energy storage state of charge constraint is shown in equation (10):
SOC,min≤SOC≤SOC,max (10)
in the formula: sOC,min，SOC,maxMinimum and maximum states of charge, S, respectivelyocAnd charging for energy storage. Preferably, the node admission area index is as shown in formula (11):
Figure GDA0001970144200000051
in the formula: the superscript t represents the time t, the subscript i represents the i node, and the following formula represents the same;
Figure GDA0001970144200000052
limiting acceptance margin for distributed photovoltaic nodes i;
Figure GDA0001970144200000053
limiting the acceptance capacity for the distributed photovoltaic nodes;
Figure GDA0001970144200000054
optimal receiving capacity for distributed photovoltaic nodes;
Figure GDA0001970144200000055
network access power for distributed photovoltaic nodes;
the optimal admission margin of the distributed photovoltaic node is as shown in formula (12):
Figure GDA0001970144200000056
in the formula:
Figure GDA0001970144200000057
optimizing an acceptance margin for the distributed photovoltaic nodes;
the frequency of the power distribution network node admission margin warning is as shown in formula (13):
Figure GDA0001970144200000058
in the formula: fnetWarning the frequency of the nodes for the admission margin; t is a scheduling period; n is the number of nodes of the power distribution network;
Figure GDA0001970144200000059
judging a 0-1 variable for the state of the node margin area, wherein the node with alarm generation at the time t is 1, and the node without alarm generation is 0;
the optimal average capacity of the power distribution network nodes is shown as the formula (14):
Figure GDA00019701442000000510
in the formula:
Figure GDA00019701442000000511
and optimally receiving the average capacity for the nodes of the power distribution network.
Preferably, the method further comprises a seventh step of: overall evaluation is carried out on the distributed photovoltaic access condition of each node of the power distribution network according to the admission margin index of each node of the power distribution network; the 4 indexes fully reflect the allowance of the power distribution network to the distributed photovoltaic from two aspects of nodes and the power distribution network; the node limit admission margin and the optimal admission margin can be respectively used as boundary conditions of a node admission margin area, and the warning frequency of the admission margin of the power distribution network and the optimal average admission capacity can reflect the capacity of the power distribution network for admitting the distributed photovoltaic.
As a preferred scheme, the distributed photovoltaic node admission area index reflects the node admission margin area of the power distribution network,
Figure GDA0001970144200000061
illustrating the photovoltaic acceptance margin in the out-of-limit zone;
Figure GDA0001970144200000062
the photovoltaic acceptance margin is shown in a good quality area;
Figure GDA0001970144200000063
indicating that the photovoltaic acceptance margin is in the warning area;
the optimal admission margin of the distributed photovoltaic nodes accurately reflects the admission margin of the high-quality areas of the nodes,
Figure GDA0001970144200000064
the node meets the optimal admission margin, and a certain margin space admits the distributed photovoltaic;
Figure GDA0001970144200000065
the optimal admission margin space is insufficient, and the economy cannot be met due to continuous admission;
the power distribution network admission margin warning node can reflect the overall situation of the optimal admission margin of the power distribution network frequently, and all the nodes in the warning area in the scheduling periodTotal number of times, FnetIf the admission margins of all nodes in the dispatching period of the power distribution network are in the high-quality area, 0 is defined;
nodes with distribution network optimal admission average capacity reflecting admission margin in premium zones, i.e. nodes
Figure GDA0001970144200000066
The average value of the optimal admission capacity of all the nodes N is an average value of the optimal admission level of each node of the power distribution network, and reflects the overall level of the admission capacity of the power distribution network.
Has the advantages that: the invention provides a distributed photovoltaic admission capacity margin optimization method for a power distribution network node. The operation personnel can know how much space of each node of the power distribution network can continue to accept the distributed photovoltaic according to the condition of the acceptance margin of the nodes in detail, and the distributed photovoltaic users of the nodes can actively optimize the network access power, so that the pressure of the power distribution network is reduced, and meanwhile, the income is increased.
According to the method, a double-layer optimization model of a power distribution network layer and a user layer is established by establishing evaluation indexes of node distributed photovoltaic, the power distribution network layer aims at the optimal operation economy of a power distribution network and the maximum optimal receiving capacity of nodes, the user layer aims at the maximum income of node distributed photovoltaic users, and the users are guided to orderly access the power distribution network and guided to perform network extension and reconstruction in a targeted manner by a power distribution network operation management department through double-layer optimization of the power distribution network and the nodes. The method is applied to the power distribution network and achieves the following beneficial effects:
(1) the provided node distributed photovoltaic limit admission margin can effectively judge an admission margin area;
(2) the optimal acceptance margin of the node can realize the accurate positioning of the acceptance margin of the power distribution network, the node with the out-of-limit acceptance margin in the power distribution network is easy to find, and the optimal acceptance margin has good reference value for an operation management department;
(3) by utilizing the coordination interaction between the power distribution network and the node users, particularly the configuration of energy storage, the optimal admission margin of the nodes of the power distribution network can be effectively improved;
(4) the margin balance coefficient introduced into the double-layer optimization model constructed by the method can effectively improve the admission margin of the node, and effectively guide the ordered network access of the distributed photovoltaic network access power of the user.
Drawings
FIG. 1 is a flow chart of a distributed photovoltaic acceptance margin optimization method of the present invention;
fig. 2 is a schematic diagram of an admission margin area of a power distribution network node.
Detailed Description
The present invention will be further described with reference to the accompanying drawings.
As shown in fig. 1, a method for optimizing a distributed photovoltaic admission capacity margin of a power distribution network node includes the following steps:
the method comprises the following steps: inputting node power of a power distribution network, line impedance, accessed distributed photovoltaic nodes and power and initial data of a network structure;
step two: calculating the distributed photovoltaic limit admission capacity of each node of the power distribution network according to the initial data;
the distributed photovoltaic limit admission capacity of each node of the power distribution network refers to that under the constraint of line current, the voltage of any node in the power distribution network reaches the maximum allowable operation voltage or the line current reaches the distributed photovoltaic capacity corresponding to the maximum allowable current through continuously increasing the photovoltaic capacity of an access node on the basis of unchanging the structure, load and distributed power supply of the existing power distribution network.
Step three: utilizing an upper-layer optimization model, optimizing the photovoltaic power of the access node by using the optimization target with the minimum network loss, the minimum power purchase of a higher-level power grid and the maximum optimal admission margin, meeting constraint conditions, and transmitting an admission margin balance coefficient to a lower-layer model;
the upper layer optimization model is shown as formula (1):
Figure GDA0001970144200000081
in the formula: t is the scheduling period, NliThe number of branches of the power distribution network;
Figure GDA0001970144200000082
power is lost for the line; pps,tThe power is purchased for the superior power grid, N is the number of nodes of the power distribution grid,
Figure GDA0001970144200000083
and optimizing the allowance for the distributed photovoltaic nodes.
The constraint conditions of the upper layer optimization model are safety constraint and admission margin constraint of the power distribution network, and the constraint conditions comprise: the method comprises the following steps of load flow equation constraint, power balance constraint, node voltage constraint, branch current constraint and limit admission margin constraint.
The power flow equation constraint is shown in formula (2):
Figure GDA0001970144200000091
in the formula: pi,t、Qi,tActive power and reactive power injected into the nodes respectively; vi,t、Vj,tNode voltages at nodes i and j, respectively; gij、BijConductance and susceptance between nodes i and j, respectively; thetaijIs the phase angle difference between nodes i and j.
The power balance constraint is shown in equation (3):
Figure GDA0001970144200000092
in the formula: n is a radical ofpvConnecting photovoltaic nodes to the power distribution network; n is a radical ofliFor the number of lines in the distribution network, Nlo＝N-NpvThe number of nodes of the power distribution network which are not connected with the photovoltaic is counted;
Figure GDA0001970144200000093
for distributed photovoltaic node network access power, the advantages transferred from the lower model to the upper modelChanging variables;
Figure GDA0001970144200000094
accessing photovoltaic node electricity purchasing power;
Figure GDA0001970144200000095
load power for the node.
The node voltage constraint is as shown in equation (4):
Vimin≤Vi,t≤Vimax (4)
in the formula: vimin，VimaxThe minimum and maximum node voltages, respectively.
The branch current constraint is shown in equation (5):
Iimin≤Ii,t≤Iimax (5)
in the formula: I.C. Aimin，IimaxRespectively, a branch current minimum and maximum.
The limit admission margin constraint is shown in equation (6):
Figure GDA0001970144200000096
in the formula:
Figure GDA0001970144200000097
is the node admission area index.
Step four: utilizing a lower-layer optimization model, carrying out photovoltaic internet power optimization by meeting constraint conditions with the maximum benefit of a node user as an optimization target, and transmitting the internet power of the node user to an upper-layer model;
the lower layer optimization model is shown in formula (7):
Figure GDA0001970144200000101
in the formula:
Figure GDA0001970144200000102
parameters for accepting the margin balance coefficient and transferring the upper layer model to the lower layer can be given according to experts and operation experience in general conditions
Figure GDA0001970144200000103
When taking 1, when
Figure GDA0001970144200000104
When is, [0,1]Taking values;
Figure GDA0001970144200000105
the photovoltaic grid-connected electricity price is obtained;
Figure GDA0001970144200000106
supplementing the photovoltaic with electricity price;
Figure GDA0001970144200000107
purchasing electricity price for the power grid;
Figure GDA0001970144200000108
photovoltaic output is taken as a node;
Figure GDA0001970144200000109
purchasing power for the node;
Figure GDA00019701442000001010
purchase cost for energy storage j;
Figure GDA00019701442000001011
the number of times of the j-th energy storage charging and discharging cycle life;
Figure GDA00019701442000001012
respectively storing energy, discharging power and charging power; alpha and beta are energy storage charging and discharging state mark parameters which are variable 0-1 and satisfy that alpha + beta is less than or equal to 1.
The constraint conditions are as follows:
the power balance constraint is shown in equation (8):
Figure GDA00019701442000001013
the node network access power constraint is shown as equation (9):
Figure GDA00019701442000001014
wherein,
Figure GDA00019701442000001017
the capacity is limited for distributed photovoltaic nodes.
The energy storage state of charge constraint is shown in equation (10):
SOC,min≤SOC≤SOC,max (10)
in the formula: s. theOC,min，SOC,maxMinimum and maximum states of charge, S, respectivelyocAnd is charged for energy storage.
Step five: after the mutual iteration of the upper layer model and the lower layer model is finished, the final optimal receiving capacity of the pre-access distributed photovoltaic node is obtained
Figure GDA00019701442000001016
Step six: calculating an evaluation index of the admission margin of the distributed photovoltaic nodes of the power distribution network, as shown in table 1:
Figure GDA0001970144200000111
table 1 shows the evaluation indexes of the node admission margins
The node admission area index is as shown in equation (11):
Figure GDA0001970144200000112
in the formula: the superscript t represents the time t, the subscript i represents the i node, and the following formula represents the same;
Figure GDA0001970144200000113
limiting acceptance margin for distributed photovoltaic nodes i;
Figure GDA0001970144200000114
limiting the acceptance capacity for the distributed photovoltaic nodes;
Figure GDA0001970144200000115
optimal receiving capacity for distributed photovoltaic nodes;
Figure GDA0001970144200000116
and accessing network power for the distributed photovoltaic nodes.
The optimal admission margin of the distributed photovoltaic node is as shown in formula (12):
Figure GDA0001970144200000117
in the formula:
Figure GDA0001970144200000118
and optimizing the allowance for the distributed photovoltaic nodes.
The frequency of the power distribution network node admission margin warning is as shown in formula (13):
Figure GDA0001970144200000119
in the formula: fnetWarning the frequency of the nodes for the admission margin; t is a scheduling period; n is the number of nodes of the power distribution network;
Figure GDA00019701442000001110
and judging a variable 0-1 for the node margin area state, wherein the node with the alarm occurrence at the time t is 1, and the node without the alarm occurrence is 0.
The optimal average capacity of the power distribution network nodes is shown as the formula (14):
Figure GDA00019701442000001111
in the formula:
Figure GDA00019701442000001112
and optimally receiving the average capacity for the nodes of the power distribution network.
Step seven: and performing overall evaluation on the distributed photovoltaic access condition of each node of the power distribution network according to the admission margin index of each node of the power distribution network. The 4 indexes fully reflect the allowance of the distribution network to the distributed photovoltaic from two aspects of the node and the distribution network. The node limit admission margin and the optimal admission margin can be respectively used as boundary conditions of a node admission margin area, and the warning frequency of the admission margin of the power distribution network and the optimal average admission capacity can reflect the capacity of the power distribution network for admitting the distributed photovoltaic.
The distributed photovoltaic node admission area index reflects the distribution network node admission margin area,
Figure GDA0001970144200000121
illustrating the photovoltaic acceptance margin in the out-of-limit zone;
Figure GDA0001970144200000122
the photovoltaic acceptance margin is shown in a good quality area;
Figure GDA0001970144200000123
illustrating the photovoltaic acceptance margin in the alert zone, see fig. 2.
The optimal admission margin of the distributed photovoltaic node accurately reflects the admission margin of a high-quality area of the node, namely reflects the optimal margin that the node can continue to admit photovoltaic.
Figure GDA0001970144200000124
The node meets the optimal admission margin, and a certain margin space admits the distributed photovoltaic;
Figure GDA0001970144200000125
it is illustrated that the optimal admission margin space is insufficient,continued acceptance fails to meet economic requirements.
The frequency of the power distribution network admission margin warning nodes can reflect the overall condition of the optimal admission margin of the power distribution network, namely the total times that all the nodes are in the warning area in the scheduling period, FnetAnd if the admission margins of all the nodes in the dispatching period of the power distribution network are in the high-quality area, the admission margins of all the nodes in the dispatching period of the power distribution network are 0.
Nodes with distribution network optimal admission average capacity reflecting admission margin in premium zones, i.e. nodes
Figure GDA0001970144200000126
The average value of the optimal admission capacity of all the nodes N is an average value of the optimal admission level of each node of the power distribution network, and reflects the overall level of the admission capacity of the power distribution network.
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.

Claims (5)

1. A distributed photovoltaic receiving capacity margin optimization method for a power distribution network node is characterized by comprising the following steps: the method comprises the following steps:
the method comprises the following steps: inputting initial data, the initial data comprising: node power, line impedance, accessed distributed photovoltaic nodes and power of the power distribution network and a network structure;
step two: calculating the distributed photovoltaic limit admission capacity of each node of the power distribution network according to the initial data;
step three: utilizing an upper-layer optimization model, optimizing the photovoltaic power of the access node by using the optimization target with the minimum network loss, the minimum power purchase of a higher-level power grid and the maximum optimal admission margin, meeting constraint conditions, and transmitting an admission margin balance coefficient to a lower-layer model; the admission margin balance coefficient is a parameter transmitted from an upper layer model to a lower layer and is given according to experts and operation experience;
step four: utilizing a lower-layer optimization model, carrying out photovoltaic internet power optimization by meeting constraint conditions with the maximum benefit of a node user as an optimization target, and transmitting the internet power of the node user to an upper-layer model;
step five: after the mutual iteration of the upper layer model and the lower layer model is finished, the final optimal receiving capacity of the pre-access distributed photovoltaic node is obtained
Figure FDA0003546851970000011
Step six: calculating an evaluation index of the acceptance margin of the distributed photovoltaic nodes of the power distribution network, wherein the evaluation index comprises the following steps: the node admission margin indexes comprise: the node acceptance area index and the optimal acceptance margin of the distributed photovoltaic nodes are obtained; the power distribution network admission margin index comprises: the power distribution network node admission margin warning frequency and the optimal average admission capacity of the power distribution network nodes are obtained;
step seven: overall evaluation is carried out on the distributed photovoltaic access condition of each node of the power distribution network according to the admission margin index of each node of the power distribution network; the 4 indexes fully reflect the allowance of the power distribution network to the distributed photovoltaic from two aspects of nodes and the power distribution network; the node limit acceptance margin and the optimal acceptance margin can be respectively used as boundary conditions of a node acceptance margin area, and the power distribution network acceptance margin warning frequency and the optimal acceptance average capacity can reflect the distributed photovoltaic acceptance capacity of the power distribution network;
the node admission area index is as shown in equation (11):
Figure FDA0003546851970000021
in the formula: the superscript t represents the time t, the subscript i represents the i node, and the following formula represents the same;
Figure FDA0003546851970000022
limiting acceptance margin for a distributed photovoltaic node i;
Figure FDA0003546851970000023
to be distributedA formula photovoltaic node ultimate acceptance capacity;
Figure FDA0003546851970000024
optimal receiving capacity for distributed photovoltaic nodes;
Figure FDA0003546851970000025
network power is accessed to the distributed photovoltaic nodes;
the optimal admission margin of the distributed photovoltaic node is as shown in formula (12):
Figure FDA0003546851970000026
in the formula:
Figure FDA0003546851970000027
optimal acceptance margin for distributed photovoltaic nodes;
the frequency of the power distribution network node admission margin warning is as shown in formula (13):
Figure FDA0003546851970000028
in the formula: fnetWarning the frequency of the nodes for the admission margin; t is a scheduling period; n is the number of nodes of the power distribution network; f. ofi tJudging a 0-1 variable for the state of the node margin area, wherein the node with alarm generation at the time t is 1, and the node without alarm generation is 0;
the optimal average capacity of the power distribution network nodes is shown as the formula (14):
Figure FDA0003546851970000029
in the formula:
Figure FDA00035468519700000210
optimal admission for distribution network nodesAverage capacity.
2. The method for optimizing the margin of the distributed photovoltaic admission capacity of the power distribution network nodes according to claim 1, characterized by comprising the following steps: the distributed photovoltaic limit admission capacity of each node of the power distribution network refers to that under the constraint of line current, the voltage of any node in the power distribution network reaches the maximum allowable operation voltage or the line current reaches the distributed photovoltaic capacity corresponding to the maximum allowable current through the continuous increase of the photovoltaic capacity of an access node on the basis of the unchanged structure, load and distributed power supply of the existing power distribution network.
3. The method for optimizing the margin of the distributed photovoltaic admission capacity of the power distribution network nodes according to claim 1, characterized by comprising the following steps: the upper optimization model is shown as formula (1):
Figure FDA0003546851970000031
in the formula: t is a scheduling period, NliThe number of branches of the power distribution network;
Figure FDA0003546851970000032
power is lost for the line; p isps,tThe power is purchased for the superior power grid, N is the number of nodes of the power distribution grid,
Figure FDA0003546851970000033
optimal acceptance margin for distributed photovoltaic nodes; f. of1Function representing the grid loss and the power purchase of the higher-level grid, f2A function representing an optimal admission margin for the distributed photovoltaic nodes;
the constraint conditions of the upper layer optimization model are safety constraint and admission margin constraint of the power distribution network, and the constraint conditions comprise: the method comprises the following steps of (1) power flow equation constraint, power balance constraint, node voltage constraint, branch current constraint and limit admission margin constraint;
the power flow equation constraint is shown in formula (2):
Figure FDA0003546851970000034
in the formula: pi,t、Qi,tRespectively injecting active power and reactive power into the node; vi,t、Vj,tNode voltages at nodes i and j, respectively; gij、BijConductance and susceptance between nodes i and j, respectively; thetaijIs the phase angle difference between nodes i and j;
the power balance constraint is shown in equation (3):
Figure FDA0003546851970000041
in the formula: n is a radical ofpvConnecting photovoltaic node numbers to the power distribution network; n is a radical ofliFor the number of lines in the distribution network, Nlo＝N-NpvThe number of nodes of the power distribution network which are not connected with the photovoltaic is counted;
Figure FDA0003546851970000042
transmitting the network access power of the distributed photovoltaic nodes to the optimization variables of the upper model for the lower model;
Figure FDA0003546851970000043
accessing photovoltaic node electricity purchasing power;
Figure FDA0003546851970000044
load power for the node;
the node voltage constraint is as shown in equation (4):
Vimin≤Vi,t≤Vimax (4)
in the formula: vimin，VimaxThe minimum value and the maximum value of the node voltage are respectively;
the branch current constraint is shown in equation (5):
Iimin≤Ii,t≤Iimax (5)
in the formula: i isimin，IimaxRespectively, the minimum and maximum values of the branch current, Ii,tRepresents the current of the branch in which the node i is positioned;
the limit admission margin constraint is shown in equation (6):
Figure FDA0003546851970000045
in the formula:
Figure FDA0003546851970000046
is the node admission area index.
4. The method for optimizing the margin of the distributed photovoltaic admission capacity of the power distribution network nodes according to claim 1, characterized by comprising the following steps: the lower optimization model is shown in formula (7):
Figure FDA0003546851970000047
in the formula:
Figure FDA0003546851970000048
for accommodating margin balance coefficients, parameters for the upper model to pass to the lower model when
Figure FDA0003546851970000049
When taking 1, when
Figure FDA00035468519700000410
When is, [0,1]Taking values;
Figure FDA00035468519700000411
the photovoltaic grid-connected electricity price is obtained;
Figure FDA00035468519700000412
is a lightThe price of the subsidy is increased;
Figure FDA00035468519700000413
purchasing electricity price for the power grid;
Figure FDA00035468519700000414
photovoltaic output is generated for the node;
Figure FDA00035468519700000415
purchasing power for the node;
Figure FDA00035468519700000416
purchase cost for stored energy j;
Figure FDA00035468519700000417
the number of times of the jth energy storage charge-discharge cycle life is counted;
Figure FDA0003546851970000051
respectively storing energy, discharging power and charging power; alpha and beta are energy storage charging and discharging state mark parameters and are variables of 0 to 1, and alpha + beta is less than or equal to 1;
Figure FDA0003546851970000052
a function representing the benefit of the node user, T represents the scheduling time, T represents the scheduling period,
Figure FDA0003546851970000053
representing the network access power of the distributed photovoltaic nodes;
the constraints are as follows:
the power balance constraint is shown in equation (8):
Figure FDA0003546851970000054
Figure FDA0003546851970000055
representing node load power;
the node network access power constraint is shown as equation (9):
Figure FDA0003546851970000056
wherein,
Figure FDA0003546851970000057
limiting the acceptance capacity for distributed photovoltaic nodes;
the energy storage state of charge constraint is shown in equation (10):
SOC,min≤SOC≤SOC,max (10)
in the formula: sOC,min，SOC,maxMinimum and maximum states of charge, S, respectivelyocAnd is charged for energy storage.
5. The method for optimizing the margin of the distributed photovoltaic admission capacity of the power distribution network nodes according to claim 1, characterized by comprising the following steps: the distributed photovoltaic node admission area index reflects the distribution network node admission margin area,
Figure FDA0003546851970000058
illustrating the photovoltaic acceptance margin in the out-of-limit zone;
Figure FDA0003546851970000059
the photovoltaic acceptance margin is shown in a good quality area;
Figure FDA00035468519700000510
indicating that the photovoltaic acceptance margin is in the alert zone;
Figure FDA00035468519700000511
representing a node admission area index;
the optimal admission margin of the distributed photovoltaic nodes accurately reflects the high quality of the nodesThe tolerance of the area to be received is,
Figure FDA00035468519700000512
the node meets the optimal admission margin, and a margin space admits the distributed photovoltaic;
Figure FDA00035468519700000513
the optimal admission margin space is insufficient, and the economy cannot be met due to continuous admission;
Figure FDA0003546851970000061
representing an optimal admission margin of the distributed photovoltaic nodes;
the frequency of the power distribution network admission margin warning nodes can reflect the overall condition of the optimal admission margin of the power distribution network, namely the total times that all the nodes are in the warning area in the scheduling period, FnetIf the admission margins of all nodes in the dispatching period of the power distribution network are in the high-quality area, 0 is defined; fnetRepresenting the frequency of admission margin warning nodes;
nodes with distribution network optimal admission average capacity reflecting admission margin in premium zones, i.e. nodes
Figure FDA0003546851970000062
The average value of the optimal admission capacity of all the nodes N is an average value of the optimal admission level of each node of the power distribution network, and reflects the overall level of the admission capacity of the power distribution network;
Figure FDA0003546851970000063
and optimally receiving the average capacity for the nodes of the power distribution network.
CN201811523562.9A 2018-12-12 2018-12-12 Distributed photovoltaic receiving capacity margin optimization method for power distribution network nodes Active CN109546683B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201811523562.9A CN109546683B (en) 2018-12-12 2018-12-12 Distributed photovoltaic receiving capacity margin optimization method for power distribution network nodes

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201811523562.9A CN109546683B (en) 2018-12-12 2018-12-12 Distributed photovoltaic receiving capacity margin optimization method for power distribution network nodes

Publications (2)

Publication Number Publication Date
CN109546683A CN109546683A (en) 2019-03-29
CN109546683B true CN109546683B (en) 2022-06-14

Family

ID=65854517

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201811523562.9A Active CN109546683B (en) 2018-12-12 2018-12-12 Distributed photovoltaic receiving capacity margin optimization method for power distribution network nodes

Country Status (1)

Country Link
CN (1) CN109546683B (en)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110445180A (en) * 2019-09-03 2019-11-12 国网河南省电力公司信阳供电公司 A kind of new-energy grid-connected method
CN111917111B (en) * 2020-07-09 2022-03-01 国网北京市电力公司 Method, system, equipment and storage medium for online evaluation of distributed photovoltaic power supply acceptance capacity of power distribution network

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5229649A (en) * 1988-09-28 1993-07-20 Solatrol, Inc. Light-energized electronics energy management system
CN105279346A (en) * 2015-11-20 2016-01-27 国网能源研究院 Method for evaluating distributed photovoltaic receiving capability of power distribution network
CN107301472A (en) * 2017-06-07 2017-10-27 天津大学 Distributed photovoltaic planing method based on scene analysis method and voltage-regulation strategy
CN107301470A (en) * 2017-05-24 2017-10-27 天津大学 A kind of power distribution network Expansion Planning stores up the dual blank-holder of addressing constant volume with light
CN107392370A (en) * 2017-07-18 2017-11-24 河海大学 The distribution network planning method containing distributed power source based on honourable correlation
CN108764552A (en) * 2018-05-21 2018-11-06 合肥工业大学 A kind of determination method of the addressing constant volume planning of power distribution network distributed generation resource

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5229649A (en) * 1988-09-28 1993-07-20 Solatrol, Inc. Light-energized electronics energy management system
CN105279346A (en) * 2015-11-20 2016-01-27 国网能源研究院 Method for evaluating distributed photovoltaic receiving capability of power distribution network
CN107301470A (en) * 2017-05-24 2017-10-27 天津大学 A kind of power distribution network Expansion Planning stores up the dual blank-holder of addressing constant volume with light
CN107301472A (en) * 2017-06-07 2017-10-27 天津大学 Distributed photovoltaic planing method based on scene analysis method and voltage-regulation strategy
CN107392370A (en) * 2017-07-18 2017-11-24 河海大学 The distribution network planning method containing distributed power source based on honourable correlation
CN108764552A (en) * 2018-05-21 2018-11-06 合肥工业大学 A kind of determination method of the addressing constant volume planning of power distribution network distributed generation resource

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
The Decomposed-Coordinating Planning Method on Distribution Network Considering the Correlation of Wind Power and PV;Linjun Shi;《2017 International Conference on Information, Communication and Engineering (ICICE)》;20171120;全文 *
通过需求侧响应提高光伏消纳率的微网二层优化方法研究;狄开丽;《电网与清洁能源》;20170330;全文 *
邱碧丹.主动配电网中分布式光伏发电优化配置研究.《水电能源科学》.2016, *

Also Published As

Publication number Publication date
CN109546683A (en) 2019-03-29

Similar Documents

Publication Publication Date Title
CN109980685B (en) Uncertainty-considered active power distribution network distributed optimization operation method
CN103997062B (en) A kind of microgrid energy management control method
CN110782363A (en) AC/DC power distribution network scheduling method considering wind power uncertainty
CN107546743B (en) Distributed power flow optimization method for radial power distribution network
CN106228258A (en) A kind of meter and the home energy source LAN energy optimal control method of dsm
CN109546683B (en) Distributed photovoltaic receiving capacity margin optimization method for power distribution network nodes
CN111987717B (en) Internet of vehicles charging and discharging management method and system, electric power system and storage medium
CN114970986A (en) Distributed power supply and energy storage collaborative planning method based on Nash equilibrium
CN115133607A (en) Method, system, equipment and medium for configuring energy storage capacity of retired battery at user side
CN114977274A (en) Multi-target inter-domain collaborative distributed optimization control method for power distribution network
CN117411085A (en) Grid-connected multi-microgrid optimal scheduling method considering external exchange power safety constraint
CN114977187B (en) Multi-main-body energy storage regulation and control method and system based on node marginal electricity price
CN116073453A (en) Alternating current-direct current hybrid power distribution network optimal scheduling method based on graph calculation
CN116093997A (en) Capacity configuration method and device for urban rail transit ground type hybrid energy storage system
CN115528670A (en) Distributed power supply and matching power grid investment decision modeling method based on multi-main game
CN113890114A (en) Mining method for flexible regulation capacity of main distribution multi-level power grid
Zhang et al. Comprehensive evaluation of ac-dc distribution network in photovoltaic-energy storage charging station based on ahp-topsis method
Li et al. Integration of prosumers' flexibilities in distribution network operation
Yue et al. Intelligent Grid Scheduling Algorithm based on Artificial Neural Network
Zhang et al. Calculation Method for Maximum Accommodation Capacity of Distributed Photovoltaic in Distribution Network Considering Various Electrical Constraints
CN113742639B (en) Method for reducing risk level of power distribution network system in multi-element main body transaction
CN118157133B (en) Active power distribution network double-layer optimal scheduling method based on electric automobile excitation strategy
CN113361864B (en) Nash equalization-based distributed collaborative optimization regulation and control method for power distribution network
CN114336656B (en) Wind farm reactive power optimization configuration method considering source load uncertainty
CN116454935A (en) Distributed energy storage access power distribution network clustering method

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant