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Optimization of a grid-tied microgrid configuration using dual storage systems
Nugraha P.Y.a, Widyotriatmo A.a, Leksono E.a
a Instrumentation and Control Program, Institut Teknologi Bandung, Bandung, 40132, 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 Institute of Control, Robotics and Systems – ICROS.This paper presents optimization in capacity sizing and operational schedule for a grid-tied microgrid using dual storage systems. Photovoltaic power system is used as a renewable energy source. Pumped-storage hydroelectricity and battery energy storage system are utilized as energy storage systems. The physical models of the photovoltaic system, pumped-storage hydroelectricity, and battery energy storage system are developed. The objective function is formulated to minimize capital and operational costs of grid-tied microgrid system. The constraints for the optimization are formulated based on the system model, operational limitations, and performance requirements. Capacity and operational schedule for the microgrid system is optimized using solar insolation data and load demand data. Performances required for microgrid system are high renewable energy penetration with low curtailed renewable energy. Mixed integer linear programming (MILP) is used to solve the optimization problem. With the proposed method, renewable energy penetration ratio achieves 50%. The curtailed renewable energy ratio of 10.4% is obtained.[/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]Battery,MILP,Photovoltaic,Pumped Storage,Sizing[/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]Battery,MILP,Optimization,Photovoltaic,Pumped-storage Hydroelectricity,Scheduling,Sizing[/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/ICCAS.2015.7364896[/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]