Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

An optimization method for transmission network planning under new energy access

An optimization method and technology for power transmission network, applied in the field of power transmission network, can solve the problems that the convergence of results needs to be improved, easy to local convergence, instability, etc., and achieve the effect of improving convergence accuracy and calculation efficiency, diversifying planning results, and improving planning efficiency.

Active Publication Date: 2020-06-09
CHENGDU CHENGDIAN ELECTRIC POWER ENG DESIGN
View PDF4 Cites 0 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] However, the overall convergence speed of the multi-objective particle swarm optimization algorithm depends on the setting of the initial parameters of the particles, which has great randomness; the genetic algorithm (GA) is complex in calculation, unstable, and prone to local convergence; NSGA-II is for two The solution of non-dominated sorting, the solution in a smaller crowded area is better), repeatedly select the sub-population generated by the basic operation of the genetic algorithm from the parent population, until the condition is met, the NSGA-Ⅱ algorithm is more complicated, and the convergence of the results is still to be determined. improve

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • An optimization method for transmission network planning under new energy access
  • An optimization method for transmission network planning under new energy access
  • An optimization method for transmission network planning under new energy access

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0125] Embodiment 1: A transmission grid system with 8 nodes and 12 corridors figure 2 as an example. Equation 4 is the adjacency matrix of the transmission grid system, Equation 5 is the expansion matrix of the transmission grid system, Equation 6 is the grid parameter matrix of the transmission grid system, and Equation 7 is composed of the information of the number of lines contained in the corridor branches of the transmission grid system population individuals.

[0126] Node 4 is an isolated node; Node 5 and Node 6 form an island;

[0127] Then formula 4:

[0128]

[0129] In the adjacency matrix L 8×8 in, l 12 =2 means that there are two lines connecting node 1 and node 2. By analogy, we can see that l 18 = l 81 =1 means that there is one line connecting node 1 and node 8.

[0130] Formula 5:

[0131]

[0132] In the adjacency matrix K 8×8 middle, k 12 =2 means that there is at most one line between node 1 and node 2 that can be expanded.

[0133] Form...

Embodiment 2

[0139] Embodiment two: test in 18-node transmission network system, it includes 18 nodes, 27 corridors such as image 3 . The system parameters are set as follows: the load of the entire transmission network system obeys a normal distribution, and the standard deviation is 2% of the expected value; a 1000MW fan is connected to node 13, and the cut-in wind speed of the fan is v ci =3m / s, rated wind speed v r =15m / s, cut out wind speed v co =25m / s; wind speed adopts two-parameter Weibull distribution model, shape parameter k=2.80, scale parameter c=5.14; control parameters Cost per unit length of each branch road and corridor C i =100, (10,000 yuan / km)r i Approximate to the reactance of a single line, the lower limit of the power flow of a branch line P l =0MVA, the upper limit of power flow for a single branch line The parameters of the MOEAD optimization algorithm are set as follows: 100 random seeds, 20 populations, 10 neighborhoods, and 500 iterations.

[0140] Tabl...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses an optimization method for planning of a power transmission network under new energy access. The optimization method comprises the steps of S100, building a power transmissionnetwork planning model containing new energy; S200, building a corresponding power transmission network topology according to a population individual in a MOEAD optimization algorithm; S300, repairingthe power transmission network topology, and enabling the power transmission network topology to be connected; S400, obtaining power flow of each branch of a power transmission network correspondingto the population individual by a probability DC power flow method, deleting the population individual which does not conform to a power transmission network planning model constraint condition, and obtaining the final population individual by iteration for many times; and S500, converting the final population individual to a planning scheme. By the optimization method, the influence of fluctuation generated by the new energy accessed to the power transmission network on planning of the power transmission network is prevented, the planning efficiency is improved, and the convergence speed andthe calculation speed of the optimization algorithm are improved.

Description

technical field [0001] The invention belongs to the technical field of transmission network, and in particular relates to an optimization method of transmission network planning under the access of new energy. Background technique [0002] New energy represented by wind power is developing at a high speed, but due to the randomness and volatility of new energy power generation itself, the transmission network connected to wind power also has volatility. The deterministic power flow calculation adopted by the conventional transmission network cannot well consider the influence of wind power on the fluctuation of the transmission network. The probabilistic DC power flow calculation can take this into consideration and is relatively simple and easy to implement. [0003] At present, the more common planning models of transmission network including wind power start from the perspective of economy and safety of the model. There are various optimization algorithms for the transm...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Patents(China)
IPC IPC(8): H02J3/00H02J3/38G06Q50/06
CPCY02E40/70Y04S10/50
Inventor 汪小明严居斌李松涛苟旭丹张琳陈瀚林张雪霞白小龙孙波黄燕李萌聂伎苡周玮郭智祺
Owner CHENGDU CHENGDIAN ELECTRIC POWER ENG DESIGN
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com