Series division method based on PSO and FA to optimize Long-Term Hydro Generation Scheduling
The fundamental requirement of power system hydro scheduling is to determine the optimal amount of generated powers for the hydro unit of the system in the scheduling horizon of 1 year or few years while satisfying the constraints of the hydroelectric system. Long-Term Hydro Generation Scheduling...
Main Authors: | , |
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Format: | Article |
Language: | English |
Published: |
Elsevier
2018
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Subjects: | |
Online Access: | http://umpir.ump.edu.my/id/eprint/23313/ http://umpir.ump.edu.my/id/eprint/23313/ http://umpir.ump.edu.my/id/eprint/23313/ http://umpir.ump.edu.my/id/eprint/23313/1/Series%20division%20method%20based%20on%20PSO%20and%20FA%20to%20optimize.pdf |
Summary: | The fundamental requirement of power system hydro scheduling is to determine the optimal amount of generated
powers for the hydro unit of the system in the scheduling horizon of 1 year or few years while satisfying
the constraints of the hydroelectric system. Long-Term Hydro Generation Scheduling (LHGS) is a complicated
nonlinear, non-convex and nonsmooth optimization problem with discontinuous solution space. The model
considers daily water inflows, limits on reservoir level, power generation depends on the available head of hydro
units caused by power variations, start-up, and shut-down of hydro units. To deal with this complicated problem,
Series division method (SDM) based on the practical swarm optimization and the firefly algorithm is proposed in
this paper. The SDM is to make a division on the Swarm Intelligence (SI) algorithm which is to be a number of
particles searching collections that properly can be regarded as divisions. Whereas, each division is a developmental
algorithm which used to get the global point. The extent of the SDM is often offered a quicker convergence
so as to accomplish the best initial operation to swarm's algorithm research. The proposed SDM are
tested on two test systems actual observed system operator (AOSO) and Standard System Operation (SSO) and
compared with some recent research works in the area. The results point out the Series Division Firefly
Algorithm (SDFA) is robust and has good efficiency and superiority. |
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