Paper Info
Title
Online optimal power flow with renewables
Abstract
Optimal power flow (OPF) is a critical control task for reliable and efficient operation of power grids. Significant challenges are anticipated in the development of future power systems, as a substantial amount of inherently uncertain renewable resources are incorporated, imposing volatile dynamics to the grid. In this work, an online learning approach, which does not require elaborate models for uncertainty, yet is capable of providing a provable performance guarantee, is adopted to tackle the OPF with renewables in an online fashion. A two-stage procedure is considered, where the conventional generation level is committed before the renewable output is revealed, followed by spot market transactions to account for imbalance. Simulated tests with a 30-bus case show that, under high variability of renewables, the proposed hedging scheme beats a static alternative, which solves two OPF problems per time slot.
Year
DOI
Venue
2014
10.1109/ACSSC.2014.7094462
ACSSC
Keywords
Field
DocType
load flow,spot market transaction,opf,learning (artificial intelligence),online optimal power flow,renewable energy sources,power grid reliable operation,power engineering computing,power grids,two-stage procedure,power markets,renewable energy resource,power generation reliability,online learning approach
Online learning,Mathematical optimization,Renewable energy,Power flow,Computer science,Electric power system,Renewable resource,Hedge (finance),Grid,Spot market
Conference
ISSN
Citations 
PageRank 
1058-6393
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Seung-Jun Kim1100362.52
G. B. Giannakis2114641206.47
Kwang Y. Lee300.34