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Energies 2015, 8(4), 2924-2949; doi:10.3390/en8042924

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

1
Urban Development Institute, Incheon National University, Incheon 406-772, Korea
2
Division of Architecture & Urban Planning, Incheon National University, Incheon 406-772, Korea
3
Centre for Sustainable Architecture and Building System Research, School of Architecture, Chung-Ang University, Seoul 156-756, Korea
4
Department of Architectural Engineering, Suwon University, Hwaseong 445-743, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Chi-Ming Lai
Received: 3 February 2015 / Revised: 20 March 2015 / Accepted: 3 April 2015 / Published: 16 April 2015
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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. View Full-Text
Keywords: hybrid energy system; genetic algorithm; multi-objective optimization; life cycle cost; penetration of renewable energy; greenhouse gas emissions hybrid energy system; genetic algorithm; multi-objective optimization; life cycle cost; penetration of renewable energy; greenhouse gas emissions
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Ko, M.J.; Kim, Y.S.; Chung, M.H.; Jeon, H.C. Multi-Objective Optimization Design for a Hybrid Energy System Using the Genetic Algorithm. Energies 2015, 8, 2924-2949.

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