Setpoint Tracking with Partially Observed Loads

Antoine Lesage-Landry; Joshua A. Taylor

doi:10.1109/TPWRS.2018.2804353

Setpoint Tracking with Partially Observed Loads

Antoine Lesage-Landry, Joshua A. Taylor

Research output: Contribution to journal › Article › peer-review

19 Scopus citations

Abstract

We use online convex optimization for setpoint tracking with uncertain, flexible loads. We consider full feedback from the loads, bandit feedback, and two intermediate types of feedback, partial bandit where a subset of the loads are individually observed and the rest are observed in aggregate, and Bernoulli feedback where in each round the aggregator receives either full or bandit feedback according to a known probability. We give sublinear regret bounds in all cases. We numerically evaluate our algorithms on examples with thermostatically controlled loads and electric vehicles.

Original language	English (US)
Article number	8288694
Pages (from-to)	5615-5627
Number of pages	13
Journal	IEEE Transactions on Power Systems
Volume	33
Issue number	5
DOIs	https://doi.org/10.1109/TPWRS.2018.2804353
State	Published - Sep 2018
Externally published	Yes

All Science Journal Classification (ASJC) codes

Energy Engineering and Power Technology
Electrical and Electronic Engineering

Keywords

Demand response
electric vehicles
online convex optimization
thermostatically controlled loads

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10.1109/TPWRS.2018.2804353

Cite this

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title = "Setpoint Tracking with Partially Observed Loads",

abstract = "We use online convex optimization for setpoint tracking with uncertain, flexible loads. We consider full feedback from the loads, bandit feedback, and two intermediate types of feedback, partial bandit where a subset of the loads are individually observed and the rest are observed in aggregate, and Bernoulli feedback where in each round the aggregator receives either full or bandit feedback according to a known probability. We give sublinear regret bounds in all cases. We numerically evaluate our algorithms on examples with thermostatically controlled loads and electric vehicles.",

keywords = "Demand response, electric vehicles, online convex optimization, thermostatically controlled loads",

author = "Antoine Lesage-Landry and Taylor, {Joshua A.}",

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AB - We use online convex optimization for setpoint tracking with uncertain, flexible loads. We consider full feedback from the loads, bandit feedback, and two intermediate types of feedback, partial bandit where a subset of the loads are individually observed and the rest are observed in aggregate, and Bernoulli feedback where in each round the aggregator receives either full or bandit feedback according to a known probability. We give sublinear regret bounds in all cases. We numerically evaluate our algorithms on examples with thermostatically controlled loads and electric vehicles.

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KW - electric vehicles

KW - online convex optimization

KW - thermostatically controlled loads

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ER -

Setpoint Tracking with Partially Observed Loads

Abstract

All Science Journal Classification (ASJC) codes

Keywords

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