Minimization of load variance in power grids-investigation on optimal vehicle-to-grid scheduling
Battery management systems; Electric automobiles; Electric vehicles; Energy management; Fleet operations; Genetic algorithms; Global warming; MATLAB; Optimization; Scheduling; Software testing; Vehicle performance; Vehicle-to-grid; Carbon emissions; Grid optimization algorithms; Optimal scheduling;...
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2023
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my.uniten.dspace-230572023-05-29T14:37:37Z Minimization of load variance in power grids-investigation on optimal vehicle-to-grid scheduling Tan K.M. Ramachandaramurthy V.K. Yong J.Y. Padmanaban S. Mihet-Popa L. Blaabjerg F. 56119108600 6602912020 56119339200 18134802000 6506881488 7004992352 Battery management systems; Electric automobiles; Electric vehicles; Energy management; Fleet operations; Genetic algorithms; Global warming; MATLAB; Optimization; Scheduling; Software testing; Vehicle performance; Vehicle-to-grid; Carbon emissions; Grid optimization algorithms; Optimal scheduling; Optimization algorithms; Research proposals; Smart Grid technologies; Transportation sector; Variance minimization; Electric power transmission networks The introduction of electric vehicles into the transportation sector helps reduce global warming and carbon emissions. The interaction between electric vehicles and the power grid has spurred the emergence of a smart grid technology, denoted as vehicle-to grid-technology. Vehicle-to-grid technology manages the energy exchange between a large fleet of electric vehicles and the power grid to accomplish shared advantages for the vehicle owners and the power utility. This paper presents an optimal scheduling of vehicle-to-grid using the genetic algorithm to minimize the power grid load variance. This is achieved by allowing electric vehicles charging (grid-to-vehicle) whenever the actual power grid loading is lower than the target loading, while conducting electric vehicle discharging (vehicle-to-grid) whenever the actual power grid loading is higher than the target loading. The vehicle-to-grid optimization algorithm is implemented and tested in MATLAB software (R2013a, MathWorks, Natick, MA, USA). The performance of the optimization algorithm depends heavily on the setting of the target load, power grid load and capability of the grid-connected electric vehicles. Hence, the performance of the proposed algorithm under various target load and electric vehicles' state of charge selections were analysed. The effectiveness of the vehicle-to-grid scheduling to implement the appropriate peak load shaving and load levelling services for the grid load variance minimization is verified under various simulation investigations. This research proposal also recommends an appropriate setting for the power utility in terms of the selection of the target load based on the electric vehicle historical data. � 2017 by the authors. Final 2023-05-29T06:37:36Z 2023-05-29T06:37:36Z 2017 Article 10.3390/en10111880 2-s2.0-85036664848 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85036664848&doi=10.3390%2fen10111880&partnerID=40&md5=20b1112186e02e419d05447441a0d032 https://irepository.uniten.edu.my/handle/123456789/23057 10 11 1880 All Open Access, Gold, Green MDPI AG Scopus |
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Battery management systems; Electric automobiles; Electric vehicles; Energy management; Fleet operations; Genetic algorithms; Global warming; MATLAB; Optimization; Scheduling; Software testing; Vehicle performance; Vehicle-to-grid; Carbon emissions; Grid optimization algorithms; Optimal scheduling; Optimization algorithms; Research proposals; Smart Grid technologies; Transportation sector; Variance minimization; Electric power transmission networks |
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56119108600 |
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56119108600 Tan K.M. Ramachandaramurthy V.K. Yong J.Y. Padmanaban S. Mihet-Popa L. Blaabjerg F. |
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Article |
| author |
Tan K.M. Ramachandaramurthy V.K. Yong J.Y. Padmanaban S. Mihet-Popa L. Blaabjerg F. |
| spellingShingle |
Tan K.M. Ramachandaramurthy V.K. Yong J.Y. Padmanaban S. Mihet-Popa L. Blaabjerg F. Minimization of load variance in power grids-investigation on optimal vehicle-to-grid scheduling |
| author_sort |
Tan K.M. |
| title |
Minimization of load variance in power grids-investigation on optimal vehicle-to-grid scheduling |
| title_short |
Minimization of load variance in power grids-investigation on optimal vehicle-to-grid scheduling |
| title_full |
Minimization of load variance in power grids-investigation on optimal vehicle-to-grid scheduling |
| title_fullStr |
Minimization of load variance in power grids-investigation on optimal vehicle-to-grid scheduling |
| title_full_unstemmed |
Minimization of load variance in power grids-investigation on optimal vehicle-to-grid scheduling |
| title_sort |
minimization of load variance in power grids-investigation on optimal vehicle-to-grid scheduling |
| publisher |
MDPI AG |
| publishDate |
2023 |
| _version_ |
1806426003202899968 |
| score |
13.214268 |