Reactive Power Optimization Model for Distribution Networks Based on the Second-Order Cone and Interval Optimization

Traditional reactive power optimization mainly considers the constraints of active management elements and ignores the randomness and volatility of distributed energy sources, which cannot meet the actual demand. Therefore, this paper establishes a reactive power optimization model for active distri...

Full description

Saved in:
Bibliographic Details
Published in:Energies (Basel) Vol. 15; no. 6; p. 2235
Main Authors: Yang, Minsheng, Li, Jianqi, Du, Rui, Li, Jianying, Sun, Jian, Yuan, Xiaofang, Xu, Jiazhu, Huang, Shifu
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.03.2022
Subjects:
Algorithms
Alternative energy sources
confidence interval estimation
Confidence intervals
Costs
Efficiency
interval optimization
Load
Normal distribution
optimal power flow
Optimization
Random variables
reactive power optimization
second-order cone relaxation
Wind power
ISSN:1996-1073, 1996-1073
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Traditional reactive power optimization mainly considers the constraints of active management elements and ignores the randomness and volatility of distributed energy sources, which cannot meet the actual demand. Therefore, this paper establishes a reactive power optimization model for active distribution networks, which is solved by a second-order cone relaxation method and interval optimization theory. On the one hand, the second-order cone relaxation technique transforms the non-convex optimal dynamic problem into a convex optimization model to improve the solving efficiency. On the other hand, the interval optimization strategy can solve the source–load uncertainty problem in the distribution network and obtain the interval solution of the optimization problem. Specially, we use confidence interval estimation to shorten the interval range, thereby improving the accuracy of the interval solution. The model takes the minimum economy as the objective function and considers a variety of active management elements. Finally, the modified IEEE 33 node arithmetic example verifies the feasibility and superiority of the interval optimization algorithm.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1996-1073
1996-1073
DOI:10.3390/en15062235