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Consuming Renewable Power: Information and Reliability as a Resource

Ram Rajagopal, Civil and Environmental Engineering

This project addresses more efficient ways of offering and consuming renewable power generation. Current approaches back up renewable energy generation so it is as reliable as traditional generators. This approach can significantly increase emissions and cost.

Instead, the researchers developed mechanisms that can benefit from existent flexibility in demand. For example, demand can be scheduled to follow renewable power profiles in real time, eliminating the necessity for reserves. The researchers have designed several smart and simple scheduling algorithms capable of following random power profiles. The researchers also designed a slotted mechanism that accepts user bids for different time slots offering available power. Appliances could be programmed with a budget and simple rules to obtain slots for each day.

Finally, the researchers investigated models that estimate available flexibility in existing demand from sensor data collected from 1,000 homes utilizing these mechanisms. This methodology revealed that the reductions in emissions required can be drastic if sufficiently large populations of schedulable loads are available. As part of the project the researchers have been building SnowFort, a wireless system capable of implementing the load-scheduling algorithms and interfacing with Zigbee radios present in most appliances and other devices.

Publications and media:

"Online modified greedy algorithm for storage control under uncertainty" IEEE Transactions on Power Systems 31 (2016): 1729-1743.

"Distributed online modified greedy algorithm for networked storage operation under uncertainty" IEEE Transactions onSmart Grid7 (2016): 1106-1118.

"Shapley value estimation for compensation of participants in demand response programs" IEEE Transactions onSmart Grid6 (2015): 2837-2844.

"Price of uncertainty in multistage stochastic power dispatch" IEEE Conference on Decision and Control (2014): 4065-4070.

"Household energy consumption segmentation using hourly data" IEEE Transactions on Smart Grid 5 (2014): 420-430.

"Cost-of-service segmentation of energy consumers" IEEE Transactions on Power Systems 29 (2014): 2795-2803.

"Smart meter driven segmentation: What your consumption says about you" IEEE Transactions on Power Systems 28 (2014): 4019-4030.

"SnowFort: An open source wireless sensor network for data analytics in infrastructure and environmental monitoring" IEEE Sensors Journal 14 (2014): 4253-4263.

"Data-driven benchmarking of building energy efficiency utilizing statistical frontier models" Journal of Computing in Civil Engineering 28 (2014): 79-88.

"A method for automatically scheduling notified deferrable loads" American Control Conference (2013): 5080-5085.

"Utility customer segmentation based on smart meter data: An empirical study" IEEE International Conference on Smart Grid Communications (SmartGridComm) (2013): 720-725.

"Demand response targeting using big data analytics" IEEE International Conference on Big Data (2013): 683-690.

"Outage detection in power distribution networks with optimally deployed power flow sensors" IEEE Power and Energy Society General Meeting (2013): 1-5.

"Smart operation of smart grid: Risk-limiting dispatch" Proceedings of the IEEE (2011): 40-57.

 

Awarded 2011 as part of the TomKat's Large-Scale Solar project.