Renewables based dynamic cost-effective optimal scheduling of distributed generators using teaching–learning-based optimization

Devarapalli, Ramesh; Sura, Srinivasa Rao

doi:10.1007/s13198-023-01864-w

Cited by 1 publication

(1 citation statement)

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“…Several studies used (ML) to solve single-objective GMS problems in electrical power systems, see Table 1. For example, reinforcement learning (RL) and teachinglearning-based optimization (TLBO) [70,71]. Benders decomposition Divide the big problem into minor problems that have much easier to solve as they require a shorter time.…”

Section: Related Workmentioning

confidence: 99%

Single Objective Optimization Methods in Electrical Power Systems: A Review

Al-kharaz¹,

A.Wahhab²,

Ibrahim³

et al. 2023

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Although the scheduling of maintenance tasks for generators is not a new issue, it has recently attracted new attention due to the significant rise in demand for expanding power system size in modern power systems. Generator Maintenance Scheduling (GMS) is a nonlinear optimization problem, highly dimensional and constrained, and determines when power-producing units must undertake well-planned preventative maintenance. The objective function includes binary variables to indicate whether a generator is undergoing maintenance at a given time and is subject to several restrictions described in this paper. However, the biggest concern of GMS is to produce a precise timetable for preventive maintenance of generating units with low cost and high reliability. Despite that, regrettably, a large volume of research works has accomplished solutions towards a model of GMS with the consideration of either maximizing system reliability or minimizing operation costs as an objective of their research work. This is called Single-Objective Problem (SOP), which involves one objective function that needs to be optimized. SOP is solved by Single-Objective Optimization Method (SOOM). The primary purpose of the research is to present a review of SOOM methods used in solving GMS problems.

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Section: Related Workmentioning

confidence: 99%