LAPSE:2023.17987
Published Article

LAPSE:2023.17987
DC Bus Voltage Selection for a Grid-Connected Low-Voltage DC Residential Nanogrid Using Real Data with Modified Load Profiles
March 7, 2023
Abstract
This study examines various low voltage levels applied to a direct current residential nanogrid (DC-RNG) with respect to the efficiency and component cost of the system. Due to the significant increase in DC-compatible loads, on-site Photovoltaic (PV) generation, and local battery storage, DC distribution has gained considerable attention in buildings. To provide an accurate evaluation of the DC-RNG’s efficiency and component cost, a one-year load profile of a conventional AC-powered house is considered, and AC appliances’ load profiles are scaled to their equivalent available DC appliances. Based on the modified load profiles, proper wiring schemes, converters, and protection devices are chosen to construct a DC-RNG. The constructed DC-RNG is modeled in MATLAB software and simulations are completed to evaluate the efficiency of each LVDC level. Four LVDC levels—24 V, 48 V, 60 V, and 120 V—are chosen to evaluate the DC-RNG’s efficiency and component cost. Additionally, impacts of adding a battery energy storage unit on the DC-RNG’s efficiency are studied. The results indicate that 60 V battery-less DC-RNG is the most efficient one; however, when batteries are added to the DC-RNG, the 48 V DC distribution becomes the most efficient and cost-effective option.
This study examines various low voltage levels applied to a direct current residential nanogrid (DC-RNG) with respect to the efficiency and component cost of the system. Due to the significant increase in DC-compatible loads, on-site Photovoltaic (PV) generation, and local battery storage, DC distribution has gained considerable attention in buildings. To provide an accurate evaluation of the DC-RNG’s efficiency and component cost, a one-year load profile of a conventional AC-powered house is considered, and AC appliances’ load profiles are scaled to their equivalent available DC appliances. Based on the modified load profiles, proper wiring schemes, converters, and protection devices are chosen to construct a DC-RNG. The constructed DC-RNG is modeled in MATLAB software and simulations are completed to evaluate the efficiency of each LVDC level. Four LVDC levels—24 V, 48 V, 60 V, and 120 V—are chosen to evaluate the DC-RNG’s efficiency and component cost. Additionally, impacts of adding a battery energy storage unit on the DC-RNG’s efficiency are studied. The results indicate that 60 V battery-less DC-RNG is the most efficient one; however, when batteries are added to the DC-RNG, the 48 V DC distribution becomes the most efficient and cost-effective option.
Record ID
Keywords
DC appliance, DC–DC converter, direct current (DC) distribution, efficiency, residential nanogrid (RNG)
Subject
Suggested Citation
Habibi S, Rahimi R, Ferdowsi M, Shamsi P. DC Bus Voltage Selection for a Grid-Connected Low-Voltage DC Residential Nanogrid Using Real Data with Modified Load Profiles. (2023). LAPSE:2023.17987
Author Affiliations
Habibi S: Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65401, USA [ORCID]
Rahimi R: Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65401, USA [ORCID]
Ferdowsi M: Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65401, USA
Shamsi P: Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65401, USA [ORCID]
Rahimi R: Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65401, USA [ORCID]
Ferdowsi M: Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65401, USA
Shamsi P: Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65401, USA [ORCID]
Journal Name
Energies
Volume
14
Issue
21
First Page
7001
Year
2021
Publication Date
2021-10-26
ISSN
1996-1073
Version Comments
Original Submission
Other Meta
PII: en14217001, Publication Type: Journal Article
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LAPSE:2023.17987
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https://doi.org/10.3390/en14217001
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