Xiaolin Hu’s scientific contributions

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Publications (1)


Fig. 1. Energy system for individual buildings.
Fig. 2. Microgrid structure and interaction with macrogrid.
Fig. 4. Heat coverage ratios in small-scale microgrids.
Fig. 5. Operation of microgrids with 3 large buildings.
Fig. 6. Heat coverage ratios in medium-scale microgrids.

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Optimal design of decentralized energy conversion systems for smart microgrids using decomposition methods
  • Article
  • Full-text available

May 2018

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274 Reads

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37 Citations

Energy

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Xiaolin Hu

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Marcus Fuchs

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The design of decentralized energy conversion systems in smart residential microgrids is a challenging optimization problem due to the variety of available generation and storage devices. Common measures to reduce the problem's size and complexity are to reduce modeling accuracy, aggregate multiple loads or change the temporal resolution. However, since these attempts alter the optimization problem and consequently lead to different solutions as intended, this paper presents and analyses a decomposition method for solving the original problem iteratively. The decomposed method is verified by comparison with the original compact model formulation, proving that both models deviate by less than 1.8%. Both approaches furthermore lead to similar energy systems that are operated similarly, as well. The findings also show that the compact model formulation is only applicable to small- and medium-scale microgrids due to current limitations of computing resources and optimization algorithms, whereas the distributed approach is suitable for even large-scale microgrids. We apply the decomposed method to a large-scale microgrid in order to evaluate economic and ecological benefits of interconnected buildings inside the grid. The results show that with local electricity exchange, costs can be reduced by 4.0% and emissions by even 23.7% for the investigated scenario.

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Citations (1)


... Mathematical optimization is an effective tool to design energy systems that are optimal, e.g., have minimal total annualized cost or global warming impact, and can be leveraged to design energy systems that are robust towards the volatility introduced by VRES ( Biegler and Grossmann, 2004;Yunt et al., 2008;Lubin et al., 2011;Li and Barton, 2015). Optimization has been successfully applied to design energy systems across various scales, from utility systems at the plant scale (Papoulias and Grossmann, 1983;Voll et al., 2013;Bahl et al., 2018; to energy systems for districts (Bünning et al., 2018;Schütz et al., 2018;Teichgraeber and Brandt, 2019) up to power systems on islands (Ma et al., 2014;Gils and Simon, 2017;Barone et al., 2021) and on the (inter)-national scale (Kannan and Turton, 2013;Siala et al., 2019;Reinert et al., 2020). For a review of modeling tools for renewable energy systems, we refer to Ringkjøb et al. (2018). ...

Reference:

Robust Energy System Design via Semi-infinite Programming
Optimal design of decentralized energy conversion systems for smart microgrids using decomposition methods

Energy