DC Bus Voltage Selection for a Grid-Connected Low-Voltage DC Residential Nanogrid Using Real Data with Modified Load Profiles

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 distri...

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Autores principales:	Saeed Habibi, Ramin Rahimi, Mehdi Ferdowsi, Pourya Shamsi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	direct current (DC) distribution residential nanogrid (RNG) DC–DC converter efficiency DC appliance Technology T
Acceso en línea:	https://doaj.org/article/875127f8c5844245b312f9d04d7fd6b5
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id	oai:doaj.org-article:875127f8c5844245b312f9d04d7fd6b5
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spelling	oai:doaj.org-article:875127f8c5844245b312f9d04d7fd6b52021-11-11T15:49:06ZDC Bus Voltage Selection for a Grid-Connected Low-Voltage DC Residential Nanogrid Using Real Data with Modified Load Profiles10.3390/en142170011996-1073https://doaj.org/article/875127f8c5844245b312f9d04d7fd6b52021-10-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7001https://doaj.org/toc/1996-1073This 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.Saeed HabibiRamin RahimiMehdi FerdowsiPourya ShamsiMDPI AGarticledirect current (DC) distributionresidential nanogrid (RNG)DC–DC converterefficiencyDC applianceTechnologyTENEnergies, Vol 14, Iss 7001, p 7001 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	direct current (DC) distribution residential nanogrid (RNG) DC–DC converter efficiency DC appliance Technology T
spellingShingle	direct current (DC) distribution residential nanogrid (RNG) DC–DC converter efficiency DC appliance Technology T Saeed Habibi Ramin Rahimi Mehdi Ferdowsi Pourya Shamsi DC Bus Voltage Selection for a Grid-Connected Low-Voltage DC Residential Nanogrid Using Real Data with Modified Load Profiles
description	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.
format	article
author	Saeed Habibi Ramin Rahimi Mehdi Ferdowsi Pourya Shamsi
author_facet	Saeed Habibi Ramin Rahimi Mehdi Ferdowsi Pourya Shamsi
author_sort	Saeed Habibi
title	DC Bus Voltage Selection for a Grid-Connected Low-Voltage DC Residential Nanogrid Using Real Data with Modified Load Profiles
title_short	DC Bus Voltage Selection for a Grid-Connected Low-Voltage DC Residential Nanogrid Using Real Data with Modified Load Profiles
title_full	DC Bus Voltage Selection for a Grid-Connected Low-Voltage DC Residential Nanogrid Using Real Data with Modified Load Profiles
title_fullStr	DC Bus Voltage Selection for a Grid-Connected Low-Voltage DC Residential Nanogrid Using Real Data with Modified Load Profiles
title_full_unstemmed	DC Bus Voltage Selection for a Grid-Connected Low-Voltage DC Residential Nanogrid Using Real Data with Modified Load Profiles
title_sort	dc bus voltage selection for a grid-connected low-voltage dc residential nanogrid using real data with modified load profiles
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/875127f8c5844245b312f9d04d7fd6b5
work_keys_str_mv	AT saeedhabibi dcbusvoltageselectionforagridconnectedlowvoltagedcresidentialnanogridusingrealdatawithmodifiedloadprofiles AT raminrahimi dcbusvoltageselectionforagridconnectedlowvoltagedcresidentialnanogridusingrealdatawithmodifiedloadprofiles AT mehdiferdowsi dcbusvoltageselectionforagridconnectedlowvoltagedcresidentialnanogridusingrealdatawithmodifiedloadprofiles AT pouryashamsi dcbusvoltageselectionforagridconnectedlowvoltagedcresidentialnanogridusingrealdatawithmodifiedloadprofiles
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DC Bus Voltage Selection for a Grid-Connected Low-Voltage DC Residential Nanogrid Using Real Data with Modified Load Profiles

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