DC micro grid energy management strategy based on a hierarchical dynamic adaptive droop control

Author(s):  
Fan Yang ◽  
Junjie Duan ◽  
Dongdong Li
2014 ◽  
Vol 18 ◽  
pp. 47-52 ◽  
Author(s):  
Tomokazu Mishima ◽  
Ittetsu Taniguchi ◽  
Hisashi Tamaki ◽  
Youichi Kitagawa ◽  
Kouji Yutani ◽  
...  

2013 ◽  
Vol 660 ◽  
pp. 139-145
Author(s):  
Lei Xue ◽  
Li Bin Wang ◽  
Zhi Gang Wang ◽  
Shu Ying Li

Energy management strategy is important to keep microgrid stable. In this paper, energy management strategy of Wind-PV-ES hybrid microgrid is proposed. Due to the power output of wind and PV are unknown quantities, the key point of Wind-PV-ES hybrid energy management lies in the energy management of storage battery. The flow charts of energy management strategy are given in detail and Wind-PV-ES microgrid model is built with DigSILENT/PowerFactory. Then the transition state simulation as to the micro-grid mode switching process is carried out. The result shows that the proposed energy management strategy could keep connected bus voltage and micro-grid frequency stable in grid-connected mode, islanding mode and during micro-grid mode switching.


Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3605
Author(s):  
Francisco David Moya ◽  
José Luis Torres-Moreno ◽  
José Domingo Álvarez

The aim of this work was to develop an optimal model for an energy management strategy in a real micro-grid, which involves a smart building, a photovoltaic system with storage, and a plug-in full electric vehicle. A controller based on a mathematical algorithm was the core of each strategy, which directly acted on a relay board managing the interconnection between the different elements comprising the micro-grid. The development of an optimization model involving binary variables required an efficient code that achieved solutions in a short time. The analyzed case-study corresponded to the solar energy research center (CIESOL) smart building, a bioclimatic building, that is located at the University of Almería (Spain), designated to research in renewable energies. Using the methodologies described in this work, the total cost of the smart building energy consumption was minimized by decreasing the power supplied from the grid, especially at peak hours. Highlighting the use of a simple model that provided better performance than the current state of the art methodologies. The optimal model for energy management strategy demonstrated the advantages of using classical optimization techniques to solve this specific optimization problem, compared to a rule-based controller. The linear modeling was capable of producing a simple algorithm with less code development and a reduction in the computational effort.


2020 ◽  
Vol 53 (2) ◽  
pp. 13012-13017
Author(s):  
Daniele Ioli ◽  
Alessandro Falsone ◽  
Axel Busboom ◽  
Maria Prandini

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