A new energy management strategy of an autonomous microgrid based on virtual impedance in multilevel droop controlled inverters

Author(s):  
M. Keddar ◽  
M.L. Doumbia ◽  
K. Belmokhtar ◽  
M. Della Krachai
2019 ◽  
Vol 43 (9) ◽  
pp. 4686-4700 ◽  
Author(s):  
Kai Ou ◽  
Wei‐Wei Yuan ◽  
Mihwa Choi ◽  
Jungsuk Kim ◽  
Young‐Bae Kim

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1380 ◽  
Author(s):  
Rui Yang ◽  
Yupeng Yuan ◽  
Rushun Ying ◽  
Boyang Shen ◽  
Teng Long

Due to the pressures caused by the energy crisis, environmental pollution, and international regulations, the largest ship-producing nations are exploring renewable resources, such as wind power, solar energy, and fuel cells to save energy and develop more environmentally-friendly ships. Solar energy has recently attracted a great deal of attention from both academics and practitioners; furthermore, the optimization of energy management has become a research topic of great interest. This paper takes a solar-diesel hybrid ship with 5000 car spaces as its research object. Then, following testing on this ship, experimental data were obtained, a multi-objective optimization model related to the ship’s fuel economy and diesel generator’s efficiency was established, and a partial swarm optimization algorithm was used to solve a multi-objective problem. The results show that the optimized energy management strategy for a hybrid energy system should be tested under different electrical loads. Moreover, the hybrid system’s economy should be taken into account when the ship’s power load is high, and the output power from the new energy generation system should be increased as much as possible. Finally, the diesel generators’ efficiency should be taken into consideration when the ship’s electrical load is low, and the injection power of the new energy system should be reduced appropriately.


2020 ◽  
Vol 69 (1) ◽  
pp. 172-181 ◽  
Author(s):  
Giuseppe Buccoliero ◽  
Pier Giuseppe Anselma ◽  
Saeed Amirfarhangi Bonab ◽  
Giovanni Belingardi ◽  
Ali Emadi

2021 ◽  
Vol 12 (4) ◽  
pp. 175
Author(s):  
Ying Huang ◽  
Fachao Jiang ◽  
Haiming Xie

The new energy of concrete truck mixers is of great significance to achieve energy conservation and emission reduction. Unlike general-purpose vehicles, in addition to driving conditions, upper-mixing system conditions, operation scenarios, and variable loads are the key factors to be considered during the new energy of concrete truck mixers. This study focuses on the machine-learning-based approximate optimal energy management design for a concrete truck mixer equipped with a novel extended-range powertrain from two aspects: trip information and energy management strategy. Firstly, an optimal control database is constructed, which benefits from a global optimization algorithm with dimension reduction for the constrained time-varying two-point boundary value problems with two control variables, and the driving data analysis through machine learning and data-driven methods. Then, different machine-learning-based driving condition identifiers are constructed and compared. Finally, a vehicle mass and power demand of an upper-part system based novel neural network energy management strategy is designed based on a constructed optimal control database. Simulation results show that the intelligent optimization algorithm based on the ML of trip information and energy management is an appropriate way to solve the online energy management problem of the concrete truck mixer equipped with the proposed novel powertrain.


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