Prairie View, Texas
March 16, 2022
March 16, 2022
March 18, 2022
9
10.18260/1-2--39201
https://peer.asee.org/39201
1403
Olatunde A. Adeoye received his BSC from the University of Lagos, Nigeria in 2005 after which he worked as a pupil engineer with the Electric Gas Turbine of the Nigeria Electric Power Authority (NEPA) and Ogun State Water Board as an Electrical Engineer till 2010. He became a member of the Nigerian Society of Engineers (NSE) in 2009 and the Council for the Regulations of Engineering Services (COREN) in 2010. He received his MSEE from the University of Texas at El Paso in 2012 where he was a graduate research assistant. He later worked at Haliburton Energy Services and worked at the Houston Independent School district as an APcalculus, Precalculus, and College Prep. Math instructor till 2019. From 2020 to the present, he is a Ph. D student at Prairie View A&M University/Electrical and Computer Engineering Department where works as a research assistant at the Center for Advancing Innovation in Smart Microgrid. His main research interests are sustainable power and energy systems, microgrids, power electronics and motor drives, digital methods for measurements control systems, and digital signal processing.
Samir I. Abood received his B.S. and M.S. from the University of Technology, Baghdad, Iraq, in 1996 and 2001; respectively, he got his Ph.D. in the Electrical and Computer Engineering Department at Prairie View A & M University. From 1997 to 2001, he worked as an engineer at the University of Technology. From 2001 to 2003, he was a professor at the University of Baghdad and Al-Nahrain University. From 2003 to 2016, Mr. Abood was a professor at Middle Technical University / Baghdad-Iraq. From 2018 to the present, he has worked at Prairie View A & M University/ Electrical and Computer Engineering Department. He is the author of 30 papers and ten books. His main research interests are sustainable power and energy systems, microgrids, power electronics and motor drives, digital PID Controllers, digital methods for electrical measurements, digital signal processing, and control systems.
Dr. Penrose Cofie is a professor in Electrical and Computer Engineering at Prairie View A and M University, College of Engineering, Texas. His research interests are in Power Systems, including Renewable Power Supplies, Power Electronics, Controls, and Motor Drives. He is currently working on Renewable Energy Generation, Micro Grid and Advanced Electric Vehicle Technology Systems.
PV Solar Battery Sizing Autonomy for Residential Applications Olatunde Adeoye, Penrose Cofie Electrical and Computer Engineering Department Prairie View A&M University Texas Abstract The major down-time of the residential photovoltaic solar systems is due to the incessant adverse weather conditions that oftentimes continue for days and sometimes weeks in some areas such as Texas in the US. This makes it impossible for designed residential photovoltaic solar systems to generate electric power that meets the electricity demand of these residences during prolonged abnormal weather conditions. Hence, battery energy storage system is utilized to back up the power supply. However, the recent severe weather freeze that collapsed the Texas grid system, has sparked attention in the renewable power generation and supply area. Designing an adequate and economical battery energy storage system to meet this irregular weather conditions has proved quite difficult since the weather throughout the year is not perfectly predictable; in fact every weather forecast is stochastic. Therefore, an energy storage design for a week’s battery autonomy in a particular year may not be adequate in another year where there is two continuous weeks of overcast weather without sunshine. This paper proposes a cost-effective strategy for battery sizing with adequate autonomy for residential photovoltaic solar systems by applying an optimization scheme using Energy Management System Flow Chart (EMSFC). The proposed method uses Lead-Acid battery and focuses on estimating average daily load, battery capacity, battery charge and discharge power limit, maximum allowable depth of discharge and number of battery modules. The reliability of this method considering weather conditions is verified using Monte Carlo simulation. References [1] Andreas Aichhorn, Michael Greenleaf, H. Li, J. Zheng, “A Cost-Effective Battery Sizing Strategy based on a detailed battery lifetime model and an economic energy management strategy”, National Sciences Foundation under Grant ECCS-1001415. IEEE 2012. [2] Chee Wei Tan, Tim C. Green, and Carlos A. Hernandez-Aramburo, “A stochastic method for battery sizing with uninterruptible-power and demand shift capabilities in PV (photovoltaic) systems”, ScienceDirect Energy Journal, September 2010, pp. 5082-5092 [3] Borowy BS, Salameh ZM, “Methodology for optimally sizing the combination of a battery bank and PV array in a wind/PV hybrid system”, IEEE Transactions on Energy Conversion 1996. [4] Dongjin Cho, Jorge Valenzuela,” Optimization of Residential Off-Grid PV-Battery Systems”, Solar Energy Journal, September 2020.
Adeoye, O. A., & Abood, S., & Cofie, P. (2022, March), PV Solar Battery Sizing Autonomy for Residential Applications Paper presented at 2022 ASEE Gulf Southwest Annual Conference, Prairie View, Texas. 10.18260/1-2--39201
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