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Optimization of capacity and operational scheduling for grid-tied microgrid using pumped-storage hydroelectricity and photovoltaic
Nugraha P.Y.a, Hadisupadmo S.a, Widyotriatmo A.a, Kurniadi D.a
a Instrumentation and Control Program, 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]© 2015 IEEE.This paper deals with the optimization of capacity and operation scheduling for a grid-tied microgrid system. Photovoltaic power system is used as renewable energy source and pumped-storage hydroelectricity is used as energy storage. The physical models of the photovoltaic system and pumped-storage hydroelectricity are developed. The objective function is formulated based on capital and operational costs of individual systems. The constraints for the optimization are defined considering the model of systems, operational limitations, and performance requirements. The capacity and the operation scheduling of the photovoltaic and pumped-storage hydroelectricity are optimized based on the solar insolation data and average load demand tied to the microgrid. The performances required are high renewable energy penetration and low curtailed renewable energy. The optimization problem is solved using the mixed integer linear problem (MILP). With the proposed scheme, the renewable energy penetration ratio achieves 50%, which is acceptable. Moreover, the curtailed renewable energy ratio of 1.22% is obtained, which is very low. Both achievements, the acceptable renewable energy penetration ratio and the low curtailed renewable energy ratio, are the key outcomes of 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=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/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]Microgrid,MILP,optimal capacity,optimal operation scheduling,optimization,photovoltaic,pumped-storage hydroelectric system,renewable energy[/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][/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/ASCC.2015.7244857[/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]