[vc_empty_space][vc_empty_space]
Optimization of capacity and operational scheduling for microgrid system using two-stage stochastic linear programming
Nugraha P.Y.a, Widyotriatmo A.a, Samsi A.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]© 2016 IEEE.This paper presents the determination of capacity and operational schedule for a grid-tied microgrid system based on a stochastic optimization method. A photovoltaic power system is used as a renewable energy source, while battery system is utilized as energy storage systems. The microgrid system can be operated using the usual priority scheme or the proposed scheduling scheme. The mathematical model for the microgrid system is developed. The objective function is formulated from a capital and operational costs. The constraints for the optimization are formulated based on system model, physical limitations, and performance requirements. Performances required for microgrid system are high renewable energy penetration with low curtailed renewable energy. Two-stage stochastic linear programming method is used to solve the optimization problem. Proposed scheduling scheme is able to increase renewable energy penetration ratio by 4% and reduce curtailed renewable energy production ratio by 7%. The combination of scheduling scheme and stochastic optimization to improve performances of microgrid system 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]Battery systems,Micro grid,Optimal capacity,Optimal operation,Photovoltaic,Renewable energies[/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 system,Microgrid,optimal capacity,Optimal operation scheduling,optimization,photovoltaic,renewable energy,stochastic programming[/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/ICA.2016.7811497[/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]