A Robust Optimization for Designing a Charging Station Based on Solar and Wind Energy for Electric Vehicles of a Smart Home in Small Villages

Abstract

In recent years, integration of electric vehicles (EVs) has increased dramatically due to their lower carbon emissions and reduced fossil fuel dependency. However, charging EVs could have significant impacts on the electrical grid. One promising method for mitigating these impacts is the use of renewable energy systems. Renewable energy systems can also be useful for charging EVs where there is no local grid. This paper proposes a new strategy for designing a renewable energy charging station consisting of wind turbines, a photovoltaic system, and an energy storage system to avoid the use of diesel generators in remote communities. The objective function is considered to be the minimization of the total net present cost, including energy production, components setup, and financial viability. The proposed approach, using stochastic modeling, can also guarantee profitable operation of EVs and reasonable effects on renewable energy sizing, narrowing the gap between real-life daily operation patterns and the design stage. The proposed strategy should enhance the efficiency of conventional EV charging stations. The key point of this study is the efficient use of excess electricity. The infrastructure of the charging station is optimized and modeled.

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• http://dx.doi.org/10.20944/preprints201806.0316.v1 under the license https://creativecommons.org/licenses/by

• http://dx.doi.org/10.3390/en11071728 under the license http://creativecommons.org/licenses/by/4.0

• https://www.mdpi.com/1996-1073/11/7/1728/pdf under the license http://creativecommons.org/licenses/by/3.0/

• https://www.preprints.org/manuscript/201806.0316/v1/download under the license cc-by

• http://www.mdpi.com/1996-1073/11/7/1728,

https://doaj.org/toc/1996-1073 under the license cc-by

• http://www.mdpi.com/1996-1073/11/7/1728/pdf,

http://dx.doi.org/10.3390/en11071728

• https://www.preprints.org/manuscript/201806.0316/v1/download,

https://ideas.repec.org/a/gam/jeners/v11y2018i7p1728-d155686.html,

https://www.preprints.org/manuscript/201806.0316,

https://academic.microsoft.com/#/detail/2809428753 under the license https://creativecommons.org/licenses/by/4.0/

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under the license https://creativecommons.org/licenses/by/4.0/

DOIS: 10.3390/en11071728 10.20944/preprints201806.0316.v1