A Nonconvex Constrained based Optimal Load Scheduling of Generators with Multiple Fuels using meta-heuristic Algorithms

The primary goal of any electric power generation system is to provide a sufficient amount of electricity to consumers without jeopardizing the system's economic viability. The modernization of the power grid has resulted in a significant rise in power demand, which has increased the cost of pr...

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Bibliographic Details
Published in2021 International Conference on Computing, Communication and Green Engineering (CCGE) pp. 1 - 5
Main Authors Rao, DSNM, Tulluri, Chiranjeevi Anil Kumar, Narukullapati, Bharath Kumar, Arshad, Haqqani, MV, Raju
Format Conference Proceeding
LanguageEnglish
Published IEEE 23.09.2021
Subjects
Costs
Education
ELD
Fuels
Multiple fuels
Non-convex
OTLBO
Propagation losses
Scheduling
Thermal loading
TLBO
Valves
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Summary:The primary goal of any electric power generation system is to provide a sufficient amount of electricity to consumers without jeopardizing the system's economic viability. The modernization of the power grid has resulted in a significant rise in power demand, which has increased the cost of producing electrical energy. When the cost of output rises, so does the cost of transferring energy to the end consumer. As a result, the output of energy at various stages of a power system must be optimized. As a result, the cost per unit of thermal energy output is reduced while load demand requirements and transmission losses are maintained. These complex non-linear quadratic functions with Multiple Fuels lead to a non-Convex problem for steam thermal generating systems, according to previous studies. Perfect Economic Load Dispatch (ELD) modelling for steam thermal generating units is possible with multiple fuels. Because acute variations and disruptions in the incremental cost function are possible, it is difficult to simplify the non-convex problem using existing techniques. Oppositional Teaching Learning Based Optimization (OTLBO) is used to address the ELD problem in this research. Under various load demands, the proposed solution was applied to a 6-unit test system, a 10-unit test system, and a 14-unit test system, and the results were evaluated using the Teaching Learning Based Optimization (TLBO) algorithm.
DOI:10.1109/CCGE50943.2021.9776402