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Control Strategy for Optimizing Energy Management in Microgrid System Using Adaptive Control

Dimas Dityagraha R.a,b,c, Choi J.b, Hariyanto N.c

a PT PLN (Persero), Jakarta, Indonesia
b School of Electrical Engineering, Chungbuk National University, Cheongju, South Korea
c School of Electrical Engineering and Informatics, Bandung Institute of Technology, 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.Recently, the development of microgrid system is growing rapidly. Energy such as sun, wind, ocean, wave currents, and geothermal are alternative energy in developing Renewable Energy Sources (RES) in the microgrid system. Most commonly hybrid RES system in the microgrid is a combination between Photovoltaic (PV), ESS and diesel generation. To implement the hybrid RES, the combination strategy control is needed. One of the main motivations using energy management in the microgrid is that they are capable of managing and coordinating diesel generators, storages, RES and loads to optimize the RES for cost efficiency. In this paper, the optimized energy management for the hybrid RES system combined by PV, Li-ion battery ESS, and diesel generator is proposed. To accommodate the diesel generation condition, the developed control strategy is the economic dispatch for fuel cost optimization in diesel generation using combination of the Lagrange multiplier and lambda iteration method and the optimization in energy management operation using an Adaptive Model Predictive Control (AMPC) with the lifetime constraint of batteries. Finally, the proposed control algorithm is verified through the PSiM simulation results.[/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]Adaptive model predictive control,Adaptive MPC,Combination strategies,Control strategies,Fuel cost,Micro grid,Optimized energy managements,Renewable energy source[/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]Adaptive MPC,Control Strategy,EMS,Fuel Cost Optimization,Microgrid[/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/ACEMP-OPTIM44294.2019.9007191[/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]