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Impacts of Load Profiles on the Optimization of Power Management of a Green Building Employing Fuel Cells

Abstract

This paper discusses the performance improvement of a green building by optimization procedures and the influences of load characteristics on optimization. The green building is equipped with a self-sustained hybrid power system consisting of solar cells, wind turbines, batteries, proton exchange membrane fuel cell (PEMFC), electrolyzer, and power electronic devices. We develop a simulation model using the Matlab/SimPowerSystemTM and tune the model parameters based on experimental responses, so that we can predict and analyze system responses without conducting extensive experiments. Three performance indexes are then defined to optimize the design of the hybrid system for three typical load profiles: the household, the laboratory, and the office loads. The results indicate that the total system cost was reduced by 38.9%, 40% and 28.6% for the household, laboratory and office loads, respectively, while the system reliability was improved by 4.89%, 24.42% and 5.08%. That is, the component sizes and power management strategies could greatly improve system cost and reliability, while the performance improvement can be greatly influenced by the characteristics of the load profiles. A safety index is applied to evaluate the sustainability of the hybrid power system under extreme weather conditions. We further discuss two methods for improving the system safety: the use of sub-optimal settings or the additional chemical hydride. Adding 20 kg of NaBH4 can provide 63 kWh and increase system safety by 3.33, 2.10, and 2.90 days for the household, laboratory and office loads, respectively. In future, the proposed method can be applied to explore the potential benefits when constructing customized hybrid power systems.

Funding source: This research was funded by the Ministry of Science and Technology, R.O.C., in Taiwan under Grands MOST 105-2622-E-002-029 -CC3, MOST 106-2622-E-002-028 -CC3, MOST 106-2221-E-002-165-, MOST 107-2221-E-002-174-, and MOST 107-2221-E-002-174-. This research was also financially supported in part by the Ministry of Science and Technology of Taiwan (MOST 107-2634-F-002-018), National Taiwan University, Center for Artificial Intelligence and Advanced Robotics.
Related subjects: Applications & Pathways
Countries: Chinese Taipei
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/content/journal2838
2018-12-25
2022-10-05
http://instance.metastore.ingenta.com/content/journal2838
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