Energy Management of Data Centers Powered by Fuel Cells and Heterogeneous Energy Storage

Author(s):  
Xiaoxuan Hu ◽  
Peng Li ◽  
Kun Wang ◽  
Yanfei Sun ◽  
Deze Zeng ◽  
...  
2019 ◽  
Vol 3 (2) ◽  
pp. 397-406 ◽  
Author(s):  
Xiaoxuan Hu ◽  
Peng Li ◽  
Kun Wang ◽  
Yanfei Sun ◽  
Deze Zeng ◽  
...  

Author(s):  
Thales Augusto Fagundes ◽  
Guilherme Henrique Favaro Fuzato ◽  
Plinio Goncalves Bueno Ferreira ◽  
Mauricio Biczkowski ◽  
Ricardo Quadros Quadros Machado

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1060
Author(s):  
Md Mamun Ur Rashid ◽  
Majed A. Alotaibi ◽  
Abdul Hasib Chowdhury ◽  
Muaz Rahman ◽  
Md. Shafiul Alam ◽  
...  

From a residential point of view, home energy management (HEM) is an essential requirement in order to diminish peak demand and utility tariffs. The integration of renewable energy sources (RESs) together with battery energy storage systems (BESSs) and central battery storage system (CBSS) may promote energy and cost minimization. However, proper home appliance scheduling along with energy storage options is essential to significantly decrease the energy consumption profile and overall expenditure in real-time operation. This paper proposes a cost-effective HEM scheme in the microgrid framework to promote curtailing of energy usage and relevant utility tariff considering both energy storage and renewable sources integration. Usually, the household appliances have different runtime preferences and duration of operation based on user demand. This work considers a simulator designed in the C++ platform to address the domestic customer’s HEM issue based on usages priorities. The positive aspects of merging RESs, BESSs, and CBSSs with the proposed optimal power sharing algorithm (OPSA) are evaluated by considering three distinct case scenarios. Comprehensive analysis of each scenario considering the real-time scheduling of home appliances is conducted to substantiate the efficacy of the outlined energy and cost mitigation schemes. The results obtained demonstrate the effectiveness of the proposed algorithm to enable energy and cost savings up to 37.5% and 45% in comparison to the prevailing methodology.


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