| Affiliation: | 1. Department of Transdisciplinary Science and Engineering, Tokyo Institute of Technology, 4259 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa, 226-8503, Japan;2. Agency for the Assessment and Application of Technology (BPPT), Puspiptek Serpong, Tangerang Selatan 15314, Indonesia;3. Department of Mechanical Engineering, University of Indonesia, Depok, 16424, Indonesia;4. Institute of Innovative Research, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan;1. Department of Chemical and Biological Engineering, Sookmyung Women''s University, Seoul, 04310, Republic of Korea;2. Department of Chemical Engineering, Hongik University, Seoul, 04066, Republic of Korea;1. State Key Laboratory of Coal Combustion, School of Energy and Power Engineering, Huazhong University of Science and Technology, Wuhan 430074, People''s Republic of China;2. Mechanical & Electrical Engineering, Henan Agricultural University, Zhengzhou 450002, China;1. College of Chemistry and Chemical Engineering, Chongqing University of Science & Technology, Chongqing, 401331, China;2. School of Chemistry and Chemical Engineering, National- Municipal Joint Engineering Laboratory for Chemical Process Intensification and Reaction, Key Laboratory of Low-Grade Energy Utilization Technologies & Systems of the Ministry of Education, Chongqing University, Chongqing, 400044, China;1. School of Electrical and Computer Engineering, National Technical University of Athens, Heroon Polytechniou 9, 15780, Zografou, Greece;2. Department of Electronics Engineering, TEI Piraeus, P. Ralli & Thivon 250, 12244, Aigaleo, Greece;3. Department of Mechanical Engineering, TEI of Western Greece, Megalou Alexandrou 1, Koukouli, Patras, Greece |
| Abstract: | In recent years, there has been considerable interest in the development of zero-emissions, sustainable energy systems utilising the potential of hydrogen energy technologies. However, the improper long-term economic assessment of costs and consequences of such hydrogen-based renewable energy systems has hindered the transition to the so-called hydrogen economy in many cases. One of the main reasons for this is the inefficiency of the optimization techniques employed to estimate the whole-life costs of such systems. Owing to the highly nonlinear and non-convex nature of the life-cycle cost optimization problems of sustainable energy systems using hydrogen as an energy carrier, meta-heuristic optimization techniques must be utilised to solve them. To this end, using a specifically developed artificial intelligence-based micro-grid capacity planning method, this paper examines the performances of twenty meta-heuristics in solving the optimal design problems of three conceptualised hydrogen-based micro-grids, as test-case systems. Accordingly, the obtained numeric simulation results using MATLAB indicate that some of the newly introduced meta-heuristics can play a key role in facilitating the successful, cost-effective development and implementation of hydrogen supply chain models. Notably, the moth-flame optimization algorithm is found capable of reducing the life-cycle costs of micro-grids by up to 6.5% as compared to the dragonfly algorithm. |
