eCite Digital Repository
Congestion management optimization in electric transmission system
Citation
Semshchikov, E and Negnevitsky, M, Congestion management optimization in electric transmission system, Proceedings of The Australasian Universities Power Engineering Conference (AUPEC 2018), 27-30 November 2018, Auckland, New Zeeland, pp. 1-5. (2018) [Refereed Conference Paper]
![]() | PDF (Congestion management optimization) 675Kb |
Copyright Statement
Copyright 2018 IEEE
Official URL: http://dx.doi.org/10.1109/AUPEC.2018.8757932
Abstract
Congestion management in electric transmission
systems is one of the most important challenges for power
systems with high penetration of renewable energy. System
congestion occurs when the desired power flow cannot be
transmitted through the network without violating system
operating limits. In order to prevent severe system damage, a
significant number of congestion management methods have
been developed, including nodal pricing, load shedding,
curtailment of renewable energy generation, generator
rescheduling, optimal transmission switching, etc. Most of these
methods, however, do not comply with the optimal operation of
conventional power plants subjected to dynamic constraints
(manoeuvrability, start-up and shut down times, etc.). In this
paper, the rescheduling generation (or re-dispatch optimization)
problem is solved using a modified particle swarm optimization
(PSO) algorithm which accounts for start up as well as shut
down times, and the manoeuvrability of conventional power
plants.
Item Details
Item Type: | Refereed Conference Paper |
---|---|
Keywords: | Congestion management, Particle swarm optimization, Transmission system |
Research Division: | Engineering |
Research Group: | Electrical engineering |
Research Field: | Electrical energy generation (incl. renewables, excl. photovoltaics) |
Objective Division: | Energy |
Objective Group: | Renewable energy |
Objective Field: | Renewable energy not elsewhere classified |
UTAS Author: | Semshchikov, E (Mr Evgenii Semshikov) |
UTAS Author: | Negnevitsky, M (Professor Michael Negnevitsky) |
ID Code: | 130791 |
Year Published: | 2018 |
Deposited By: | Engineering |
Deposited On: | 2019-02-12 |
Last Modified: | 2019-10-23 |
Downloads: | 22 View Download Statistics |
Repository Staff Only: item control page