Research on Operation Optimization of “Optical Storage Charging and Swap” Power Station Based on Robust Optimization Algorithm

It is very important for the application of artificial intelligence to accurately and quickly help the electric vehicles to find matching charging facilities. The site selection for electric vehicle charging station (EVCS) is a new field of artificial intelligence application, using artificial intelligence to analyze the current complex urban electric vehicle driving path, and then determining the location of charging stations. This paper proposes a novel hybrid model to decide the location of EVCS. First of all, this paper carries out the flow-refueling location model (FRLM) based on path requirement to determine the site selection of EVCS. Secondly, robust optimization algorithm is used to resolve the location model considering the uncertainty of charging demand. Then, queuing theory, which takes the charging load as a constraint in the location model, is integrated into the model. Last, but not the least, a case is conducted to verify the validity of the proposed model when dealing with location problem. As a result of the above analysis, it is effective to apply robust optimization algorithm and to determine the location of EVCSs effectively when charging demand generated on the path is uncertain. At the same time, queuing theory can help to determine the optimal number of EVCSs effectively, and reduce the cost of building EVCSs.

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Non-contact estimation at 60 GHz for human vital signs monitoring using a robust optimization algorithm

2016 IEEE International Symposium on Antennas and Propagation (APSURSI) ◽

10.1109/aps.2016.7696290 ◽

2016 ◽

Cited By ~ 1

Author(s):

Ting Zhangt ◽

Guido Valerio ◽

Julien Sarrazin ◽

Dan Istrate

Keyword(s):

Robust Optimization ◽

Optimization Algorithm ◽

Vital Signs ◽

60 Ghz ◽

Vital Signs Monitoring

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Multidispatch for Microgrid including Renewable Energy and Electric Vehicles with Robust Optimization Algorithm

Energies ◽

10.3390/en13112813 ◽

2020 ◽

Vol 13 (11) ◽

pp. 2813 ◽

Cited By ~ 1

Author(s):

Ruifeng Shi ◽

Penghui Zhang ◽

Jie Zhang ◽

Li Niu ◽

Xiaoting Han

Keyword(s):

Renewable Energy ◽

Robust Optimization ◽

Electric Vehicles ◽

Optimization Algorithm ◽

Environmental Cost ◽

Uncertain Variables ◽

System A ◽

Ev Charging ◽

Numerical Case Study

With the deterioration of the environment and the depletion of fossil fuel energy, renewable energy has attracted worldwide attention because of its continuous availability from nature. Despite this continuous availability, the uncertainty of intermittent power is a problem for grid dispatching. This paper reports on a study of the scheduling and optimization of microgrid systems for photovoltaic (PV) power and electric vehicles (EVs). We propose a mathematical model to address the uncertainty of PV output and EV charging behavior, and model scheduling optimization that minimizes the economic and environmental cost of a microgrid system. A semi-infinite dual optimization model is then used to deal with the uncertain variables, which can be solved with a robust optimization algorithm. A numerical case study shows that the security and stability of the solution obtained by robust optimization outperformed that of stochastic optimization.

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