A Study on the Generator Maintenance Scheduling Considering CO2 and Economical Efficiency
- Authors
- Lee, Yeonchan; Jung, Myeunghoon; Cha, Junmin; Choi, Jaeseok
- Issue Date
- 2018
- Publisher
- IEEE
- Keywords
- Generator Maintenance Scheduling; Probabilistic production cost; Probabilistic Reliability Evaluation; CO2 emission; Genetic Algorithm
- Citation
- 2018 IEEE INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS (PMAPS)
- Indexed
- SCOPUS
- Journal Title
- 2018 IEEE INTERNATIONAL CONFERENCE ON PROBABILISTIC METHODS APPLIED TO POWER SYSTEMS (PMAPS)
- URI
- https://scholarworks.gnu.ac.kr/handle/sw.gnu/13172
- Abstract
- This paper demonstrated a program that establishes Generator Maintenance Scheduling(GMS) that considers economic feasibility, environmentality, reliability, and supply reserve rate in a variety of aspects. This program aims for helping decision-makers to set up GMS by integrating various opinions using Fuzzy method. Probabilistic generation simulation in this program calculated the amounts of generation using Booth-Baleriaux Method and drew a value approximating to an optimal solution using GA in order to calculate a non-linear GMS value possessing infinite numbers. A case study set a standard value calculated by crisp in the system similar with the Korean system to aspiration level of fuzzy method and showed searching for satisfaction level by changing generation costs, the amounts of CO2 emission, LOLE, and SRR. On top of that, it established GMS and showed the condition of generation system.
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