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Multi-Objective Optimization Design for a Hybrid Energy System Using the Genetic Algorithm

Authors
Ko, Myeong JinKim, Yong ShikChung, Min HeeJeon, Hung Chan
Issue Date
Apr-2015
Publisher
MDPI AG
Keywords
Genetic algorithm; Greenhouse gas emissions; Hybrid energy system; Life cycle cost; Multi-objective optimization; Penetration of renewable energy
Citation
ENERGIES, v.8, no.4, pp 2924 - 2949
Pages
26
Journal Title
ENERGIES
Volume
8
Number
4
Start Page
2924
End Page
2949
URI
https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/64607
DOI
10.3390/en8042924
ISSN
1996-1073
Abstract
To secure a stable energy supply and bring renewable energy to buildings within a reasonable cost range, a hybrid energy system (HES) that integrates both fossil fuel energy systems (FFESs) and new and renewable energy systems (NRESs) needs to be designed and applied. This paper presents a methodology to optimize a HES consisting of three types of NRESs and six types of FFESs while simultaneously minimizing life cycle cost (LCC), maximizing penetration of renewable energy and minimizing annual greenhouse gas (GHG) emissions. An elitist non-dominated sorting genetic algorithm is utilized for multi-objective optimization. As an example, we have designed the optimal configuration and sizing for a HES in an elementary school. The evolution of Pareto-optimal solutions according to the variation in the economic, technical and environmental objective functions through generations is discussed. The pair wise trade-offs among the three objectives are also examined.
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