Paper Info
Title
Re-Scheduling The Unit Commitment Problem In Fuzzy Environment
Abstract
The conventional prediction of future power demands are always made based on the historical data. However, the real power demands are affected by many other factors as weather, temperature and unexpected emergencies. The use of historical information alone cannot well predict real future demands. In this study, the experts' opinions from related fields are taken into consideration. To deal the uncertainty of historical data and imprecise experts' opinions, we employ fuzzy variables to better characterize the forecasted future power loads. The conventional unit commitment problem (UCP) is updated here by considering the spinning reserve costs in a fuzzy environment. As the solution, we proposed a heuristic algorithm called local convergence averse binary particle swarm optimization (LCA-PSO) to solve the UCP. The proposed model and algorithm are used to analyze several test systems. The comparisons between the proposed algorithm and the conventional approaches show that the LCA-PSO performs better in finding the optimal solutions.
Year
DOI
Venue
2011
10.1109/FUZZY.2011.6007313
IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ 2011)
Keywords
Field
DocType
Power supply reliability, Fuzzy set theory, Unit commitment problem, Fuzzy Value-at-Risk, Local convergence averse binary particle swarm optimization, Test systems
Convergence (routing),Mathematical optimization,Scheduling (computing),Control theory,Computer science,Heuristic (computer science),Power system simulation,Fuzzy logic,Fuzzy set,AC power,Local convergence
Conference
ISSN
Citations 
PageRank 
1098-7584
3
0.42
References 
Authors
6
3
Name
Order
Citations
PageRank
Bo Wang1473.95
You Li2304.45
Junzo Watada341184.53