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Pricing Scheme for EV Charging Load Penetration in Distribution Network: Study Case Jakarta
Gamawanto A.a, Qinthara M.U.a, Rahman F.S.a, Banjar-Nahor K.M.a, Hariyanto N.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2019 IEEE.Indonesia is going to enter the new era of electric vehicle. In this paper, the charging load of electric vehicles (EVs) is presented along with the operational cost optimization of the distribution grid based on the trend and the development of EVs in Indonesia from 2020 until 2030. Since the development of EVs in Indonesia has just officially begun, the charging loads and demand data from several types of EVs are analyzed. With no historical data for Jakarta, Monte Carlo simulation method is proposed with 3 main scenarios for forecasting the charging load of EVs, which are reference, moderate and advanced scenario. Operational cost optimization approach is proposed in order to achieve the most preferable pricing scheme for EV charging load in the distribution network. Accordingly, the optimization will be made based on the 3 main scenarios used in the forecasting process. As a study case, Jakarta City is chosen as the main object to analyze the best pricing strategy of the development of EVs throughout 2030 as its debut to start the electric transportation era.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Advanced scenarios,Cost optimization,Distribution grid,Electric transportation,Electric Vehicles (EVs),grid,Jakarta,Monte Carlo simulation methods[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]charging,cost,EV,forecasting,grid,Jakarta,Monte Carlo,optimization[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]The author would like to thank PT Quadran Energi Rekayasa for providing the data and software needed to run the simulation in this research.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/icSmartGrid48354.2019.8990696[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]