Distributionally Robust Chance-Constrained Energy Management of Multi-Building Residential Apartment Complexes Using Wasserstein Metric
The decreasing costs of photovoltaic (PV) systems and battery storage, alongside the rapid rise of electric vehicles (EVs), present a unique opportunity to revolutionize energy use in apartment complexes. Generating electricity via PV and batteries is currently cheaper and greener than relying on gr...
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creator | Haggi, Hamed Fenton, James M |
description | The decreasing costs of photovoltaic (PV) systems and battery storage,
alongside the rapid rise of electric vehicles (EVs), present a unique
opportunity to revolutionize energy use in apartment complexes. Generating
electricity via PV and batteries is currently cheaper and greener than relying
on grid power, which is often expensive. Yet, residents in multi-building
apartment complexes typically lack access to fast EV charging infrastructure.
To this end, this paper investigates the feasibility and energy management of
deploying commercial PV-powered battery storage and EV fast chargers within
apartment complexes in Orlando, Florida, operated by complex owners. By
modeling the complex as a grid-connected microgrid, it aims to meet residents'
energy needs, provide backup power during emergencies, and introduce a
profitable business model for property owners. To address PV power generation
uncertainty, a distributionally robust chance-constrained optimization method
using the Wasserstein metric is employed, ensuring robust and reliable
operation. The techno-economic analysis reveals that EVs powered by PV and
batteries are more cost-effective and environmentally friendly than gasoline
vehicles that EV owners can save up to 100 dollars per month by saving on fuel
costs. The results also show that integrating PV and battery systems reduces
operational costs, lowers emissions, increases resilience, and supports EV
adoption while offering a profitable business model for property owners. These
findings highlight a practical and sustainable framework for advancing clean
energy use in residential complexes. |
doi_str_mv | 10.48550/arxiv.2412.00875 |
format | Article |
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alongside the rapid rise of electric vehicles (EVs), present a unique
opportunity to revolutionize energy use in apartment complexes. Generating
electricity via PV and batteries is currently cheaper and greener than relying
on grid power, which is often expensive. Yet, residents in multi-building
apartment complexes typically lack access to fast EV charging infrastructure.
To this end, this paper investigates the feasibility and energy management of
deploying commercial PV-powered battery storage and EV fast chargers within
apartment complexes in Orlando, Florida, operated by complex owners. By
modeling the complex as a grid-connected microgrid, it aims to meet residents'
energy needs, provide backup power during emergencies, and introduce a
profitable business model for property owners. To address PV power generation
uncertainty, a distributionally robust chance-constrained optimization method
using the Wasserstein metric is employed, ensuring robust and reliable
operation. The techno-economic analysis reveals that EVs powered by PV and
batteries are more cost-effective and environmentally friendly than gasoline
vehicles that EV owners can save up to 100 dollars per month by saving on fuel
costs. The results also show that integrating PV and battery systems reduces
operational costs, lowers emissions, increases resilience, and supports EV
adoption while offering a profitable business model for property owners. These
findings highlight a practical and sustainable framework for advancing clean
energy use in residential complexes.</description><identifier>DOI: 10.48550/arxiv.2412.00875</identifier><language>eng</language><subject>Computer Science - Systems and Control</subject><creationdate>2024-12</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,885</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2412.00875$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2412.00875$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Haggi, Hamed</creatorcontrib><creatorcontrib>Fenton, James M</creatorcontrib><title>Distributionally Robust Chance-Constrained Energy Management of Multi-Building Residential Apartment Complexes Using Wasserstein Metric</title><description>The decreasing costs of photovoltaic (PV) systems and battery storage,
alongside the rapid rise of electric vehicles (EVs), present a unique
opportunity to revolutionize energy use in apartment complexes. Generating
electricity via PV and batteries is currently cheaper and greener than relying
on grid power, which is often expensive. Yet, residents in multi-building
apartment complexes typically lack access to fast EV charging infrastructure.
To this end, this paper investigates the feasibility and energy management of
deploying commercial PV-powered battery storage and EV fast chargers within
apartment complexes in Orlando, Florida, operated by complex owners. By
modeling the complex as a grid-connected microgrid, it aims to meet residents'
energy needs, provide backup power during emergencies, and introduce a
profitable business model for property owners. To address PV power generation
uncertainty, a distributionally robust chance-constrained optimization method
using the Wasserstein metric is employed, ensuring robust and reliable
operation. The techno-economic analysis reveals that EVs powered by PV and
batteries are more cost-effective and environmentally friendly than gasoline
vehicles that EV owners can save up to 100 dollars per month by saving on fuel
costs. The results also show that integrating PV and battery systems reduces
operational costs, lowers emissions, increases resilience, and supports EV
adoption while offering a profitable business model for property owners. These
findings highlight a practical and sustainable framework for advancing clean
energy use in residential complexes.</description><subject>Computer Science - Systems and Control</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNqFjj1uwkAQRrehQCEHoMpcwI75sUILDiiNGwSitMZ4cEZaz1o7a4RPkGvHWOlTfcV70veMmS-SeL1J0-Qd_YPv8XK9WMZJsvlIp-bnkzV4LrvATtDaHo6u7DRA9o1ypShzMnBkoQr2Qr7uIUfBmhqSAO4GeWcDR7uObcVSw5GUqwExWti26MPoZa5pLT1I4axP64Kq5DUQC-Q0_F9nZnJDq_T6ty_m7bA_ZV_RmFy0nhv0ffFML8b01f_GL3anUiA</recordid><startdate>20241201</startdate><enddate>20241201</enddate><creator>Haggi, Hamed</creator><creator>Fenton, James M</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20241201</creationdate><title>Distributionally Robust Chance-Constrained Energy Management of Multi-Building Residential Apartment Complexes Using Wasserstein Metric</title><author>Haggi, Hamed ; Fenton, James M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-arxiv_primary_2412_008753</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computer Science - Systems and Control</topic><toplevel>online_resources</toplevel><creatorcontrib>Haggi, Hamed</creatorcontrib><creatorcontrib>Fenton, James M</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Haggi, Hamed</au><au>Fenton, James M</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Distributionally Robust Chance-Constrained Energy Management of Multi-Building Residential Apartment Complexes Using Wasserstein Metric</atitle><date>2024-12-01</date><risdate>2024</risdate><abstract>The decreasing costs of photovoltaic (PV) systems and battery storage,
alongside the rapid rise of electric vehicles (EVs), present a unique
opportunity to revolutionize energy use in apartment complexes. Generating
electricity via PV and batteries is currently cheaper and greener than relying
on grid power, which is often expensive. Yet, residents in multi-building
apartment complexes typically lack access to fast EV charging infrastructure.
To this end, this paper investigates the feasibility and energy management of
deploying commercial PV-powered battery storage and EV fast chargers within
apartment complexes in Orlando, Florida, operated by complex owners. By
modeling the complex as a grid-connected microgrid, it aims to meet residents'
energy needs, provide backup power during emergencies, and introduce a
profitable business model for property owners. To address PV power generation
uncertainty, a distributionally robust chance-constrained optimization method
using the Wasserstein metric is employed, ensuring robust and reliable
operation. The techno-economic analysis reveals that EVs powered by PV and
batteries are more cost-effective and environmentally friendly than gasoline
vehicles that EV owners can save up to 100 dollars per month by saving on fuel
costs. The results also show that integrating PV and battery systems reduces
operational costs, lowers emissions, increases resilience, and supports EV
adoption while offering a profitable business model for property owners. These
findings highlight a practical and sustainable framework for advancing clean
energy use in residential complexes.</abstract><doi>10.48550/arxiv.2412.00875</doi><oa>free_for_read</oa></addata></record> |
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subjects | Computer Science - Systems and Control |
title | Distributionally Robust Chance-Constrained Energy Management of Multi-Building Residential Apartment Complexes Using Wasserstein Metric |
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