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

Myeong Jin Ko, Yong Shik Kim, Min Hee Chung and Hung Chan Jeon
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Myeong Jin Ko: Urban Development Institute, Incheon National University, Incheon 406-772, Korea
Yong Shik Kim: Division of Architecture & Urban Planning, Incheon National University, Incheon 406-772, Korea
Min Hee Chung: Centre for Sustainable Architecture and Building System Research, School of Architecture, Chung-Ang University, Seoul 156-756, Korea
Hung Chan Jeon: Department of Architectural Engineering, Suwon University, Hwaseong 445-743, Korea

Energies, 2015, vol. 8, issue 4, 1-26

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.

Keywords: hybrid energy system; genetic algorithm; multi-objective optimization; life cycle cost; penetration of renewable energy; greenhouse gas emissions (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24)

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