CN108681786B - Distributed power generation site selection planning method based on power supply community structure - Google Patents
Distributed power generation site selection planning method based on power supply community structure Download PDFInfo
- Publication number
- CN108681786B CN108681786B CN201810372041.1A CN201810372041A CN108681786B CN 108681786 B CN108681786 B CN 108681786B CN 201810372041 A CN201810372041 A CN 201810372041A CN 108681786 B CN108681786 B CN 108681786B
- Authority
- CN
- China
- Prior art keywords
- power supply
- nodes
- power generation
- node
- 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
Links
Images
Classifications
-
- 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"
- G06Q10/043—Optimisation of two dimensional placement, e.g. cutting of clothes or wood
-
- 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/06—Electricity, gas or water supply
-
- 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
- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
Abstract
The invention discloses a distributed power generation site selection planning method based on a power supply community structure, which comprises the following steps: defining equivalent power supply correlation strength between two nodes by taking a composite weight of an electrical distance between a power generation node and a load node and effective transmission capacity in a power grid, wherein the power supply correlation strength between the two power generation nodes, between the two load nodes, between the power generation and transmission nodes and between the load and transmission nodes is 0; dividing the power grid into a plurality of community structures, and calculating a power supply association strength matrix of the nodes of the whole network of the power grid according to the equivalent power supply association strength between the two nodes; and calculating the difference of the power supply association strength between all the power generation nodes and the load nodes in a known network and a random network, and taking the power generation node distribution scheme with the maximum difference value as an optimal solution. The power supply modularization index is defined as a quantitative index of a power supply community structure construction mode, is applied to site selection planning and evaluation of distributed power generation, and can meet the dynamic balance requirement of continuous development of load and distributed power generation.
Description
Technical Field
The invention relates to transformation and planning of a traditional power distribution network, in particular to a distributed power generation site selection planning method based on a power supply community structure.
Background
With the introduction of the concept of energy internet, the construction of a user-side energy network or an energy local area network becomes an important subject for the development of the energy internet, however, the resource allocation and operation mode of the existing power distribution network are built according to early planning, and the resource allocation and operation mode cannot meet the free, equivalent, flexible and autonomous targets of the energy internet. One of the major challenges facing the construction of smart grids and energy internets is the deployment and development of distributed generation in traditional power distribution networks.
At present, how to plan the deployment implementation of distributed power generation is still lack of a uniform and effective method. In practice, it is often based on voluntary random deployment by the user, lacking optimization based on the needs of the full network and long-term development. The existing students try to carry out site selection planning on distributed generation based on the existing load operation simulation of a network and the principles of maximum income or minimum network loss and the like. However, as the load and power generation resources are in continuous growth and change, the methods are difficult to account for the long-term dynamic development requirements of the system, and the safety and stability requirements of the system are not fully considered.
The demand of efficient, safe and stable operation of the power distribution network is analyzed from the future, and the overall power distribution network gradually changes to a regional self-balancing power supply mode. If the original power distribution network is divided into a plurality of virtual microgrids, self-power balance is realized to the maximum extent in the virtual microgrids. In this way, the network loss is minimized, and the system's ability to resist attacks and failures is also enhanced. For uncertainty of changes of load and origin resources, the structure of the network is relatively stable, and the method is an important basis for planning and site selection to realize self-balance.
In a complex network theory, a method for dividing a network into a plurality of community structures based on a network topology structure exists, for example, a Newman fast partitioning algorithm defines a modular parameter modulation, and quantifies the dividing quality. For example, as shown in fig. 1, based on the network structure, it is apparent that the network can be divided into two communities, as indicated by the dashed circles. However, the analysis is based on the demand of power supply self-balancing, wherein all generators are arranged in one community structure, and all loads are arranged in the other community structure. Therefore, the power supply relationship from the generator to the load is all across the community structure, without any power supply relationship within the community structure, which is a typical non-power supply community structure.
Disclosure of Invention
In view of the above technical problems, the present invention aims to: the method defines a power supply modularization index as a quantitative index for measuring the structure mode of the power supply community structure, and carries out site selection distribution of distributed power generation with the aim of maximizing the power supply modularization. The method is simple and feasible for site selection planning and evaluation of distributed power generation, and can meet the dynamic balance requirement of continuous development of load and distributed power generation.
The technical scheme of the invention is as follows:
a distributed power generation site selection planning method based on a power supply community structure comprises the following steps:
s01: defining equivalent power supply correlation strength between two nodes by taking a composite weight of an electrical distance between a power generation node and a load node and effective transmission capacity in a power grid, wherein the power supply correlation strength between the two power generation nodes, between the two load nodes, between the power generation and transmission nodes and between the load and transmission nodes is 0;
s02: dividing the power grid into a plurality of community structures, and calculating a power supply association strength matrix of the nodes of the whole network of the power grid according to the equivalent power supply association strength between the two nodes;
s03: and calculating the difference of the power supply association strength between all the power generation nodes and the load nodes in a known network and a random network, and taking the power generation node distribution scheme with the maximum difference value as an optimal solution.
In a preferred technical solution, the equivalent power supply correlation strength in step S01 is:
wherein, alpha and beta are weights, ZvwRepresents the equivalent electrical distance, C, between node v and node wvwRepresenting the effective transmission capacity between node v and node w,
for the normalization index based on the whole net average,
and
corresponding full net averages.
In a preferred embodiment, in step S03, a difference between the known network and the random network in the power supply correlation strength between all the power generation nodes and the load nodes is defined as a power supply modularization index, where the power supply modularization index is:
if Cv＝CwThen, delta (C)v,Cw) 1, otherwise, δ (C)v,Cw)＝0；
Wherein M is the total power supply correlation quantity of the whole network
AvThe total amount of power supply associated for node v,
Av＝∑wAvw，Awtotal amount of power supply association for node w, AvwTo supply the elements of the strength matrix A, Cv，CwIs the region to which the nodes v, w belong.
Compared with the prior art, the invention has the advantages that:
according to the method, a community structure with close internal electrical association and sparse external is obtained according to a relatively stable power grid structure, and then the distributed power generation facilities are deployed by site selection according to the principle of maximizing power supply modularization. Therefore, the advantages of the network structure are utilized, the inherent self-balancing power supply relationship in the community structure is realized, and the influence of the variation and uncertainty in the operation of the power generation facility and the load on the self-balancing capability is reduced to the minimum. Fully realizes high-efficiency, safe and stable operation. When an emergency situation occurs, the whole network can be disconnected according to the structure of the power supply communities, and each power supply community still has high self-balancing capability after the disconnection.
Drawings
The invention is further described with reference to the following figures and examples:
FIG. 1 is a schematic diagram of a non-powered community architecture;
FIG. 2 is a schematic diagram of a power community structure;
fig. 3 is a flowchart of the distributed power generation site selection planning method based on the power supply community structure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings in conjunction with the following detailed description. It should be understood that the description is intended to be exemplary only, and is not intended to limit the scope of the present invention. Moreover, in the following description, descriptions of well-known structures and techniques are omitted so as to not unnecessarily obscure the concepts of the present invention.
According to the requirement of self-balancing power supply, the network can be clearly divided into a plurality of community structures according to the structure. In each community structure, there are power generation nodes and load nodes, and the power supply relationship in the community is obviously tighter than the power supply relationship across the community structure, which is the power supply community structure, as shown in fig. 2.
The full network nodes are divided into three categories: a power generation node, a load node, a transmission node (non-power generation non-load node).
Setting a power generation node v and a load node w, and in order to describe the magnitude of electrical connection between the nodes, taking a composite weight of two characteristic quantities, namely an electrical distance (impedance) between the nodes and an effective transmission capacity, to define equivalent power supply association strength between any two nodes v and the nodes w:
wherein, alpha and beta are weights, ZvwRepresents the equivalent electrical distance, C, between node v and node wvwRepresenting the effective transmission capacity between node v and node w,
for the normalization index based on the whole net average,
and
corresponding full net averages.
The physical meaning of the equivalent power supply correlation strength is that the smaller the electrical distance between two nodes is, the smaller the transmission loss is; the larger the transmission capacity between two nodes, the larger the maximum power that can be transmitted. Therefore, the smaller the electrical distance between two nodes, the greater the transmission capacity, and the greater the power supply correlation strength.
The power supply correlation strength between other two power generation nodes, between two load nodes, between the power generation and transmission nodes and between the load and transmission nodes is set to 0.
Thus, based on the definition of the electrical correlation strength between two points, for a given grid G, a power supply correlation strength matrix a, a can be obtained that describes the electrical structural characteristics thereofvw＝EvwI.e. representing the elements in matrix a. Unlike the definition of the adjacent matrix in the complex network theory, only the power generation nodes and the load nodes in the matrixThe elements between the points are non-zero, and the corresponding elements between any other two points are all zero, because there is no power supply relationship between them.
According to the definition of the power supply correlation strength matrix, the total power supply correlation amount of the whole network is defined as:
for node v, the total power supply association amount is:
Av＝∑wAvw
and w is any other node in the power grid.
For a given grid G, if the whole network is divided into several community structures, CvAnd CwRespectively representing the community structures to which the node v and the node w belong, the power supply modularization index describing the division mode can be defined as:
if Cv＝CwThen, delta (C)v,Cw) 1, otherwise, δ (C)v,Cw)＝0。
The physical meaning of the power supply modularization index can be understood as: for a given network G, if a unit of power association strength is randomly drawn among all association strengths, the probability that this unit of power association strength connects node v and node w depends on two events:
a. the unit power supply association strength starts at node v;
b. the unit power association strength terminates at node w.
For node v, the total power supply association amount is AvThen the probability of event a is Avand/2M. Since the strength of the power association between node v and node w is known in a given network X, event a and event b are not completely independent events. When event a is true, the probability of event b is Avw/Av. Then, randomly extractingThe probability that the unit power supply correlation strength is connected from the node v to the node w should be (A)v/2M)·(Avw/Av)＝Avw/2M。
Correspondingly, for another random network Y, the total amount of nodes and power supply association strength of the network Y is the same as that of the network G, and the total amount of power supply association strength of each node is also the same as that of the network G, but lines with different power supply association strengths are randomly distributed in the network Y. Then a unit power supply correlation strength is randomly drawn and the probability that the unit power supply correlation strength is connected from node v to node w still depends on both events a, b. Similar to its case in network G, the probability of event a is avand/2M. However, since the power supply correlation strength is randomly distributed in the network Y, the power supply correlation strength between the node v and the node w cannot be taken as a known condition, the event a and the event b are completely independent events, and the probability of the event b is awand/2M. Thus, the randomly drawn probability that the unit power supply correlation strength is connected from node v to node w is (A)v/2M)·(Aw/2M)。
It can be seen from this that the supply modularity index describes: the difference in the strength of the power association between all the generation nodes and the load nodes in the known network and the random network for the same area. If this difference is larger, the closer the electrical connection between the generation and load nodes in the area is justified. The more reasonable the way these nodes are divided into a community structure. Therefore, the definition of the power supply modularization can be used as a basis for measuring the rationality of the self-balancing power supply division mode. If the numerical value of the power supply modularization is larger, the closer the electrical association between the power generation and the load in the divided region is, and the more sparse the association between the power generation and the load across the region is.
Finding the optimal distributed generation deployment site is then equivalent to finding the Kmax deployment result.
Example (b):
the steps of the invention are shown in figure 3:
1. firstly, for a traditional power distribution network, only loads in the network are not accessed by distributed power generation equipment, and parameters such as impedance and transmission capacity of all lines are determined according to structural parameters of the network.
2. Calculating equivalent impedance and equivalent transmission capacity among all nodes:
Zvw＝Z′w-2Z′vw+Zww
wherein Z ' vv (Z ' ww) is the element of the v (w) th row and v (w) th column of the impedance matrix, and Z ' vw is the element of the v (v) th row and w (w) th column of the impedance matrix. PlmaxRepresents the maximum Power that the transmission line i is allowed to deliver under normal conditions, F represents the Power Transfer Distribution Factor, Fl vwWhich represents the change in power flow on the transmission line i when electrical energy of a unit power flows in from the node v and out from the node w.
3. According to specific conditions and requirements, the power grid is divided into a plurality of community structures by using a pure topology or weighted community structure identification algorithm, such as a Newman quick partitioning algorithm and the like.
4. And (3) assuming that the total number of the planning access distributed power generation equipment is G, randomly selecting G nodes from the whole network, and assuming that the nodes are power generation nodes.
5. Calculating a power supply association strength matrix A of the whole network:
Avw＝Evwi.e. representing the elements in matrix a.
Only the elements between the power generation node and the load node in the matrix are nonzero, and the corresponding elements between any other two points are zero because no power supply relation exists between the elements.
6. Calculating the total power supply association amount of the whole network according to the power supply association strength matrix:
for node v, the total power supply association amount is:
Av＝∑wAvw
and w is any other node in the power grid.
7. Calculating and recording a corresponding power supply modularization index value:
if Cv＝CwThen, delta (C)v,Cw) 1, otherwise, δ (C)v,Cw)＝0
8. And repeating the steps 4 to 7 and recording the corresponding power supply modularization index value K until all feasible G power generation node combinations are traversed.
9. And selecting the power generation node distribution scheme with the maximum K value as an optimal solution.
It is to be understood that the above-described embodiments of the present invention are merely illustrative of or explaining the principles of the invention and are not to be construed as limiting the invention. Therefore, any modification, equivalent replacement, improvement and the like made without departing from the spirit and scope of the present invention should be included in the protection scope of the present invention. Further, it is intended that the appended claims cover all such variations and modifications as fall within the scope and boundaries of the appended claims or the equivalents of such scope and boundaries.
Claims (3)
1. A distributed power generation site selection planning method based on a power supply community structure is characterized by comprising the following steps:
s01: defining equivalent power supply correlation strength between two nodes by taking a composite weight of an electrical distance between a power generation node and a load node and effective transmission capacity in a power grid, wherein the power supply correlation strength between the two power generation nodes, between the two load nodes, between the power generation and transmission nodes and between the load and transmission nodes is 0;
s02: dividing the power grid into a plurality of community structures, and calculating a power supply association strength matrix of the nodes of the whole network of the power grid according to the equivalent power supply association strength between the two nodes;
s03: and calculating the difference of the power supply association strength between all the power generation nodes and the load nodes in a known network and a random network, and taking the power generation node distribution scheme with the maximum difference value as an optimal solution.
2. The power supply community structure-based distributed power generation siting planning method according to claim 1, wherein the strength of the equivalent power supply association in step S01 is:
wherein, alpha and beta are weights, ZvwRepresents the equivalent electrical distance, C, between node v and node wvwRepresenting the effective transmission capacity between node v and node w,
for the normalization index based on the whole net average,
and
corresponding full net averages.
3. The power supply community structure-based distributed power generation siting planning method according to claim 1, wherein in step S03, the difference between the power supply association strength between all power generation nodes and load nodes in the known network and the random network is defined as a power supply modularization index, and the power supply modularization index is:
if Cv＝CwThen, delta (C)v,Cw) 1, otherwise, δ (C)v,Cw)＝0；
Wherein M is the total power supply correlation quantity of the whole network
AvThe total amount of power supply associated for node v,
Av＝∑wAvw，Awtotal amount of power supply association for node w, AvwTo supply the elements of the strength matrix A, Cv，CwIs the region to which the nodes v, w belong.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810372041.1A CN108681786B (en) | 2018-04-24 | 2018-04-24 | Distributed power generation site selection planning method based on power supply community structure |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810372041.1A CN108681786B (en) | 2018-04-24 | 2018-04-24 | Distributed power generation site selection planning method based on power supply community structure |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108681786A CN108681786A (en) | 2018-10-19 |
| CN108681786B true CN108681786B (en) | 2021-11-09 |
Family
ID=63801305
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810372041.1A Active CN108681786B (en) | 2018-04-24 | 2018-04-24 | Distributed power generation site selection planning method based on power supply community structure |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108681786B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111146815B (en) * | 2018-11-05 | 2023-10-13 | 西交利物浦大学 | Distributed power generation planning configuration method for intelligent power distribution network |
| CN111080022A (en) * | 2019-12-23 | 2020-04-28 | 国网四川省电力公司经济技术研究院 | Partition distributed coordination optimization method containing multiple benefit agents |
| CN113193552B (en) * | 2021-04-28 | 2022-09-27 | 青岛理工大学 | Power grid wiring method suitable for point-to-point electric energy transmission mode |
| CN113420994B (en) * | 2021-06-28 | 2022-10-18 | 山东大学 | Method and system for evaluating structure flexibility of active power distribution network |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103971183A (en) * | 2014-05-27 | 2014-08-06 | 华北电力大学 | Optimal addressing and capacity configuration method for photovoltaic power station |
| CN105303460A (en) * | 2015-10-30 | 2016-02-03 | 国家电网公司 | Identification method of key nodes and key branches in power grid |
| CN107508315A (en) * | 2017-08-24 | 2017-12-22 | 南京南瑞继保电气有限公司 | The power distribution network isolated island division methods of meter and the equivalent electrical distance in power supply group inside and outside |
-
2018
- 2018-04-24 CN CN201810372041.1A patent/CN108681786B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103971183A (en) * | 2014-05-27 | 2014-08-06 | 华北电力大学 | Optimal addressing and capacity configuration method for photovoltaic power station |
| CN105303460A (en) * | 2015-10-30 | 2016-02-03 | 国家电网公司 | Identification method of key nodes and key branches in power grid |
| CN107508315A (en) * | 2017-08-24 | 2017-12-22 | 南京南瑞继保电气有限公司 | The power distribution network isolated island division methods of meter and the equivalent electrical distance in power supply group inside and outside |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108681786A (en) | 2018-10-19 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108681786B (en) | Distributed power generation site selection planning method based on power supply community structure | |
| CN106026092B (en) | It is a kind of for the power distribution network isolated island division methods containing distributed generation resource | |
| Yang et al. | A novel slow coherency based graph theoretic islanding strategy | |
| CN109636009B (en) | Method and system for establishing neural network model for determining line loss of power grid | |
| Dall’Anese et al. | Risk-constrained microgrid reconfiguration using group sparsity | |
| CN106067678A (en) | Improve the system and method for stability of power system | |
| Wang et al. | Weighted and constrained consensus for distributed power dispatch of scalable microgrids | |
| CN113705085A (en) | Modeling and risk assessment method for multi-level structure of smart power grid | |
| CN114638433A (en) | Load recovery distribution robust optimization method considering wind power uncertainty | |
| Duong et al. | Available transfer capability determination for the electricity market using cuckoo search algorithm | |
| CN105790256B (en) | Power distribution network access distributed generation unit critical path recognition methods based on multi-agent technology | |
| CN108206542B (en) | Energy internet construction method based on structure compactness | |
| Changchao et al. | Research on the frequency synchronization control strategy for power system | |
| Wang et al. | Distribution system planning incorporating distributed generation and cyber system vulnerability | |
| Matavalam et al. | Curriculum based reinforcement learning of grid topology controllers to prevent thermal cascading | |
| Li et al. | Evaluation of critical node groups in cyber-physical power systems based on pinning control theory | |
| Zhu et al. | Bi‐level optimised emergency load/generator shedding strategy for AC/DC hybrid system following DC blocking | |
| Sun et al. | Assessing wind curtailment under different wind capacity considering the possibilistic uncertainty of wind resources | |
| Torres et al. | Spectral graph theory and network dependability | |
| Wang et al. | Cascading failure analysis and robustness assessment of the operational system and electric power system based on dependent network | |
| Asvapoositkul et al. | Analysis of the variables influencing inter-area oscillations in the future Great Britain power system | |
| Tajalli et al. | Maximizing social welfare considering the uncertainty of wind power plants using a distributed consensus-based algorithm | |
| Iwata et al. | Multi-population differential evolutionary particle swarm optimization for distribution state estimation using correntropy in electric power systems | |
| Afzalan et al. | Placement and sizing of DG using PSO&HBMO algorithms in radial distribution networks | |
| Sang et al. | An Initialization-free Distributed Algorithm for Power Dispatch Problem with Multiple Resources of Future Distribution Network |
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 |