Metaheuristic Optimization in Dynamic Unit Commitment of Power Systems: Current Trends and Future Prospects

This paper discusses various optimization algorithms, a mathematics and computer science method essential to solving challenging real-world problems. The presented real-world scenario centers on a power system with dynamic unit commitment. Power systems involve many variables and constraints that can be changed. Metaheuristic optimization can be an effective method for finding an excellent solution to a problem. Dynamic unit commitment optimization is a critical aspect of power system operation. Dynamic unit commitment discusses determining the operating schedule of generating units and how much their production capacity is at each specific time interval to minimize production costs. This research aims to investigate the current trends and future challenges in applying metaheuristic optimization algorithms to power system problems, using the PRISMA approach to conduct a systematic literature review (SLR). This research starts by formulating research questions: what metaheuristic methods can be employed to tackle dynamic unit commitment challenges in power systems, and how effective are these methods in optimization? We established specific criteria for including and excluding studies, such as choosing articles published in English and concentrating on unit commitment in power systems. Out of the 487 papers discovered from the abstracts, only 14 were chosen for review after an extensive screening process based on complete studies that specifically addressed dynamic unit commitment. The conducted analysis provided insights into the accuracy and efficiency levels attained when using metaheuristic algorithms to solve the dynamic unit commitment problem. The research underscores the pivotal role of metaheuristic optimization in modern energy and power systems, highlighting its significance in tackling diverse challenges associated with enhancing the efficiency and effectiveness of power system utilization. Using metaheuristics in academic research has significantly improved scientific publications, resulting in an average annual increase of 43% in citations. This research's real contribution to the academic field is to provide an alternative reference for approximately 66 ASEAN universities with power system engineering majors.

Authors

Institutions

Publication Details

Journal
JOIV International Journal on Informatics Visualization
Published
2024-12-31
DOI
https://doi.org/10.62527/joiv.8.4.2423
Citations
1
Primary Topic
Power Systems and Renewable Energy
Type
article
Field-Weighted Citation Impact
0.17
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Metaheuristic Optimization in Dynamic Unit Commitment of Power Systems: Current Trends and Future Prospects

Lilis Widayanti, Heru Wahyu Herwanto, Vivi Aida Fitria, Arif Nur Afandi
1 citations
JOIV International Journal on Informatics Visualization
Power Systems and Renewable Energy
0.17
article

Metaheuristic Optimization in Dynamic Unit Commitment of Power Systems: Current Trends and Future Prospects

Lilis Widayanti, Heru Wahyu Herwanto, Vivi Aida Fitria, Arif Nur Afandi
article en
1 citations

Abstract

This paper discusses various optimization algorithms, a mathematics and computer science method essential to solving challenging real-world problems. The presented real-world scenario centers on a power system with dynamic unit commitment. Power systems involve many variables and constraints that can be changed. Metaheuristic optimization can be an effective method for finding an excellent solution to a problem. Dynamic unit commitment optimization is a critical aspect of power system operation. Dynamic unit commitment discusses determining the operating schedule of generating units and how much their production capacity is at each specific time interval to minimize production costs. This research aims to investigate the current trends and future challenges in applying metaheuristic optimization algorithms to power system problems, using the PRISMA approach to conduct a systematic literature review (SLR). This research starts by formulating research questions: what metaheuristic methods can be employed to tackle dynamic unit commitment challenges in power systems, and how effective are these methods in optimization? We established specific criteria for including and excluding studies, such as choosing articles published in English and concentrating on unit commitment in power systems. Out of the 487 papers discovered from the abstracts, only 14 were chosen for review after an extensive screening process based on complete studies that specifically addressed dynamic unit commitment. The conducted analysis provided insights into the accuracy and efficiency levels attained when using metaheuristic algorithms to solve the dynamic unit commitment problem. The research underscores the pivotal role of metaheuristic optimization in modern energy and power systems, highlighting its significance in tackling diverse challenges associated with enhancing the efficiency and effectiveness of power system utilization. Using metaheuristics in academic research has significantly improved scientific publications, resulting in an average annual increase of 43% in citations. This research's real contribution to the academic field is to provide an alternative reference for approximately 66 ASEAN universities with power system engineering majors.

JOIV International Journal on Informatics VisualizationVol. 8(4)
State University of Malang (ID), Institut Teknologi Nasional Malang (ID)
Openalex Percentile: Top 40%
Power Systems and Renewable Energy
0.17
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.