Abstract

Residences with smart home energy management (HEM) systems and solar generation are modifying domestic load profiles. Moreover, the growing penetration of solar photovoltaic (PV) energy brings the total net power demand further down as houses become local generators. High PV penetration introduces technical challenges for the power system including the "duck curve". This paper proposes a co-simulation framework for high PV penetration smart energy communities which allows the simultaneous simulation of home energy consumption along with control algorithms for each house, as well as system power flow. Models are developed and presented for one of the largest rural field demonstrators for smart energy technologies comprising industrial, business, and 5,000+ residences, located in Glasgow, KY, US. The objectives of the HEM system are to reduce the total energy consumption and peak demand by controlling the heating ventilation and air-conditioning (HVAC) systems, water heaters, and batteries, so as to benefit both consumers and the utility. The advantages to the residential consumers include reduced electricity bills and the utility benefits from lower peak demand. Case studies are conducted for typical winter and summer days and simulation and experimental results are presented. The paper also includes long term load prediction for the utility considering different percentages of smart homes.

Document Type

Conference Proceeding

Publication Date

9-2019

Notes/Citation Information

Published in 2019 IEEE Energy Conversion Congress and Exposition (ECCE).

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The document available for download is the authors’ manuscript version that is accepted for publication. The final published version is copyrighted by IEEE and available as: H. Gong, V. Rallabandi, M. L. McIntyre, and D. M.Ionel, “On the Optimal Energy Controls for Large Scale Residential Communities including Smart Homes,” Rec. 2019 IEEE Energy Conversion Congress and Exposition (ECCE), Baltimore, MD, Sept 2019, pp. 503-507, doi: 10.1109/ECCE.2019.891249

Digital Object Identifier (DOI)

https://doi.org/10.1109/ECCE.2019.8912490

Funding Information

The support of University of Kentucky, the L. Stanley Pigman endowment, as well as the information provided by the Glasgow Electric Plant Board (EPB) are gratefully acknowledged.

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