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

• Main Authors: Myeong Jin Ko, Yong Shik Kim, Min Hee Chung, Hung Chan Jeon
• Format: Article
• Language: English
• Published: MDPI AG 2015-04-01
• Series: Energies
• Subjects: hybrid energy system; genetic algorithm; multi-objective optimization; life cycle cost; penetration of renewable energy; greenhouse gas emissions
• Online Access: http://www.mdpi.com/1996-1073/8/4/2924

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.