Operating Schedule of Battery Energy Storage System in a Time-of-Use Rate Industrial User With Wind Turbine Generators: A Multipass Iteration Particle Swarm Optimization Approach

This paper presents a new algorithm for the solution of nonlinear optimal scheduling problems. This algorithm is called ldquomultipass iteration particle swarm optimizationrdquo (MIPSO). A new index called ldquoiteration bestrdquo is incorporated into ldquoparticle swarm optimizationrdquo (PSO) to i...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on energy conversion 2007-09, Vol.22 (3), p.774-782
Main Author: Lee, Tsung-Ying
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper presents a new algorithm for the solution of nonlinear optimal scheduling problems. This algorithm is called ldquomultipass iteration particle swarm optimizationrdquo (MIPSO). A new index called ldquoiteration bestrdquo is incorporated into ldquoparticle swarm optimizationrdquo (PSO) to improve solution quality. The concept of multipass dynamic programming is applied to further modify the PSO to improve computation efficiency. The MIPSO algorithm is used to solve the optimal operating schedule of a battery energy storage system (BESS) for an industrial time-of-use (TOU) rate user with wind turbine generators (WTGs). The effects of wind speed uncertainty and load are considered in this paper, and the resulting optimal operating schedule of the BESS reaches the minimum electricity charge of TOU rates users with WTGs. The feasibility of the new algorithm is demonstrated by a numerical example, and MIPSO solution quality and computation efficiency are compared to those of other algorithms.
ISSN:0885-8969
1558-0059
DOI:10.1109/TEC.2006.878239