Hierarchical coordination of trains and traction substation storages for energy cost optimization

Author(s):  
Hrvoje Novak ◽  
Vinko Lesic ◽  
Mario Vasak

2018 ◽  
Vol 10 (4) ◽  
pp. 1118 ◽  
Author(s):  
Yungyu Chang ◽  
Gyewoon Choi ◽  
Juhwan Kim ◽  
Seongjoon Byeon


2021 ◽  
Vol 9 ◽  
Author(s):  
Yan Liao ◽  
Yong Liu ◽  
Chaoyu Chen ◽  
Lili Zhang

In this research, we propose a multi-objective optimization framework to minimize the energy cost while maintain the indoor air quality. The proposed framework is consisted with two stages: predictive modeling stage and multi-objective optimization stage. In the first stage, artificial neural networks are applied to predict the energy utility in real-time. In the second stage, an optimization algorithm namely firefly algorithm is utilized to reduce the energy cost while maintaining the required IAQ conditions. Industrial data collected from a commercial building in central business district in Chengdu, China is utilized in this study. The results produced by the optimization framework show that this strategy reduces energy cost by optimizing operations within the HAVC system.







2009 ◽  
Vol 48 (13) ◽  
pp. 6010-6021 ◽  
Author(s):  
Aihua Zhu ◽  
Panagiotis D. Christofides ◽  
Yoram Cohen


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