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Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations

Al-Bahrani, Loau, Seyedmahmoudian, Mehdi, Horan, Ben and Stojcevski, Alex 2021, Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations, Sustainability, vol. 13, no. 3, pp. 1-26, doi: 10.3390/su13031274.

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Title Solving the Real Power Limitations in the Dynamic Economic Dispatch of Large-Scale Thermal Power Units under the Effects of Valve-Point Loading and Ramp-Rate Limitations
Author(s) Al-Bahrani, Loau
Seyedmahmoudian, Mehdi
Horan, BenORCID iD for Horan, Ben orcid.org/0000-0002-6723-259X
Stojcevski, Alex
Journal name Sustainability
Volume number 13
Issue number 3
Article ID 1274
Start page 1
End page 26
Total pages 26
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2021-01-26
ISSN 2071-1050
Keyword(s) dynamic economic dispatch
valve-point loading
ramp-rate limitations
multi-gradient PSO algorithm
real power of large-scale thermal power units
Science & Technology
Life Sciences & Biomedicine
Green & Sustainable Science & Technology
Environmental Sciences
Environmental Studies
Science & Technology - Other Topics
Environmental Sciences & Ecology
Summary Few non-traditional optimization techniques are applied to the dynamic economic dispatch (DED) of large-scale thermal power units (TPUs), e.g., 1000 TPUs, that consider the effects of valve-point loading with ramp-rate limitations. This is a complicated multiple mode problem. In this investigation, a novel optimization technique, namely, a multi-gradient particle swarm optimization (MG-PSO) algorithm with two stages for exploring and exploiting the search space area, is employed as an optimization tool. The M particles (explorers) in the first stage are used to explore new neighborhoods, whereas the M particles (exploiters) in the second stage are used to exploit the best neighborhood. The M particles’ negative gradient variation in both stages causes the equilibrium between the global and local search space capabilities. This algorithm’s authentication is demonstrated on five medium-scale to very large-scale power systems. The MG-PSO algorithm effectively reduces the difficulty of handling the large-scale DED problem, and simulation results confirm this algorithm’s suitability for such a complicated multi-objective problem at varying fitness performance measures and consistency. This algorithm is also applied to estimate the required generation in 24 h to meet load demand changes. This investigation provides useful technical references for economic dispatch operators to update their power system programs in order to achieve economic benefits.
Language eng
DOI 10.3390/su13031274
Indigenous content off
Field of Research 12 Built Environment and Design
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2021, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30147793

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.