淡江大學機構典藏:Item 987654321/124270
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/124270


    Title: An advanced complementary scheme of floating photovoltaic and hydropower generation flourishing water-food-energy nexus synergies
    Authors: Chang, Li-chiu
    Date: 2020-06-18
    Issue Date: 2023-07-19 12:06:21 (UTC+8)
    Abstract: Hybrid hydropower and floating photovoltaic power generation has far-reaching effects on the intertwined water, food and energy (WFE) nexus, but the complementary operation is fundamentally challenging especially under high uncertainties of hydro-meteorological conditions. This study proposed an artificial intelligence-based WFE system-overarching solution driven by hybrid hydro-floating photovoltaic power generation for promoting nexus synergies. A multi-objective optimization model grounded upon the Grasshopper Optimization Algorithm was developed to simultaneously maximize hydro-floating photovoltaic power output, the ratio of water storage to reservoir capacity, and the ratio of water supply to water demand. The Shihmen Reservoir watershed and its WFE system in northern Taiwan constituted the case study. The results demonstrated that the proposed optimization model could significantly improve synergistic benefits of the WFE nexus by reaching 13%, 13.3% and 15.1% in water storage, food production and hydro-floating photovoltaic power output, respectively. The optimal tilt angles of floating photovoltaic installation would vary between −11.9° (Summer) and 44.3° (Winter). This study opens up new perspectives on green energy production expansion while stimulating WFE nexus synergies in support of policy-makers with feasible schemes on floating photovoltaic deployment in the interest of social sustainability. In consequence, new niches are exploited for floating photovoltaic deployment and give rise to impact mitigation concerning hydro-meteorological uncertainties on WFE nexus management.
    Relation: Applied Energy 275, 115389
    DOI: 10.1016/j.apenergy.2020.115389
    Appears in Collections:[Department of Artificial Intelligence] Journal Article

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