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Cyber-Phys. Syst."],"published-print":{"date-parts":[[2017,4,30]]},"abstract":"<jats:p>Renewable energy (e.g., solar energy) is an attractive option to provide green energy to homes. Unfortunately, the intermittent nature of renewable energy results in a mismatch between when these sources generate energy and when homes demand it. This mismatch reduces the efficiency of using harvested energy by either (i) requiring batteries to store surplus energy, which typically incurs \u223c 20% energy conversion losses, or (ii) using net metering to transmit surplus energy via the electric grid\u2019s AC lines, which severely limits the maximum percentage of renewable penetration possible. In this article, we propose an alternative structure where nearby homes explicitly share energy with each other to balance local energy harvesting and demand in microgrids. We develop a novel energy sharing approach to determine which homes should share energy, and when to minimize system-wide energy transmission losses in the microgrid. We evaluate our approach in simulation using real traces of solar energy harvesting and home consumption data from a deployment in Amherst, MA. We show that our system (i) reduces the energy loss on the AC line by 64% without requiring large batteries, (ii) performance scales up with larger battery capacities, and (iii) is robust to different energy consumption patterns and energy prediction accuracy in the microgrid.<\/jats:p>","DOI":"10.1145\/2823355","type":"journal-article","created":{"date-parts":[[2017,2,3]],"date-time":"2017-02-03T10:19:26Z","timestamp":1486117166000},"page":"1-22","source":"Crossref","is-referenced-by-count":12,"title":["Minimizing Transmission Loss in Smart Microgrids by Sharing Renewable Energy"],"prefix":"10.1145","volume":"1","author":[{"given":"Zhichuan","family":"Huang","sequence":"first","affiliation":[{"name":"University of Maryland, Baltimore County"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ting","family":"Zhu","sequence":"additional","affiliation":[{"name":"University of Maryland, Baltimore County"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Irwin","sequence":"additional","affiliation":[{"name":"University of Massachusetts, Amherst, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aditya","family":"Mishra","sequence":"additional","affiliation":[{"name":"Seattle University, WA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Menasche","sequence":"additional","affiliation":[{"name":"Federal University of Rio de Janeiro, RJ, Brasil"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Prashant","family":"Shenoy","sequence":"additional","affiliation":[{"name":"University of Massachusetts, Amherst, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2016,12,19]]},"reference":[{"key":"e_1_2_1_1_1","first-page":"240","article-title":"Reducing transient and steady state electricity consumption in HVAC using learning-based model predictive control","volume":"100","author":"Anil A.","year":"2011","unstructured":"A. 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