Power management in energy harvesting sensor networks
ACM Transactions on Embedded Computing Systems (TECS) - Special Section LCTES'05
Leakage-aware energy synchronization for wireless sensor networks
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Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
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eShare: a capacitor-driven energy storage and sharing network for long-term operation
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IGCC '11 Proceedings of the 2011 International Green Computing Conference and Workshops
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ICCPS '12 Proceedings of the 2012 IEEE/ACM Third International Conference on Cyber-Physical Systems
ICDCS '12 Proceedings of the 2012 IEEE 32nd International Conference on Distributed Computing Systems
The case for efficient renewable energy management in smart homes
Proceedings of the Third ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
Energy-synchronized computing for sustainable sensor networks
Ad Hoc Networks
Incentivizing Advanced Load Scheduling in Smart Homes
Proceedings of the 5th ACM Workshop on Embedded Systems For Energy-Efficient Buildings
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Renewable energy harvested from the environment is an attractive option for providing 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 ~20% energy conversion losses; or ii) using net metering to transmit surplus energy via the electric grid's AC lines, which severely limits the maximum percentage of possible renewable penetration. In this paper, we propose an alternative structure wherein 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 efficiency losses. 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 60% without requiring large batteries, ii) scales up performance with larger battery capacities, and iii) is robust to changes in microgrid topology.