Decentralised Control of Micro-Storage in the Smart Grid
Decentralised Control of Micro-Storage in the Smart Grid
In this paper, we propose a novel decentralised control mechanism to manage micro-storage in the smart grid. Our approach uses an adaptive pricing scheme that energy suppliers apply to home smart agents controlling micro-storage devices. In particular, we prove that the interaction between a supplier using our pricing scheme and the actions of selfish micro-storage agents forms a globally stable feedback loop that converges to an efficient equilibrium. We further propose a market strategy that allows the supplier to reduce wholesale purchasing costs without increasing the uncertainty and variance for its aggregate consumer demand. Moreover, we empirically evaluate our mechanism (based on the UK grid data) and show that it yields savings of up to 16% in energy cost for consumers using storage devices with average capacity 10 kWh. Furthermore, we show that it is robust against extreme system changes.
1421-1426
Voice, Thomas
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Vytelingum, Perukrishnen
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Ramchurn, Sarvapali
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Rogers, Alex
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Jennings, Nick
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2011
Voice, Thomas
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Vytelingum, Perukrishnen
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Ramchurn, Sarvapali
1d62ae2a-a498-444e-912d-a6082d3aaea3
Rogers, Alex
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Jennings, Nick
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Voice, Thomas, Vytelingum, Perukrishnen, Ramchurn, Sarvapali, Rogers, Alex and Jennings, Nick
(2011)
Decentralised Control of Micro-Storage in the Smart Grid.
AAAI-11: Twenty-Fifth Conference on Artificial Intelligence, San Francisco, United States.
07 - 11 Aug 2011.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
In this paper, we propose a novel decentralised control mechanism to manage micro-storage in the smart grid. Our approach uses an adaptive pricing scheme that energy suppliers apply to home smart agents controlling micro-storage devices. In particular, we prove that the interaction between a supplier using our pricing scheme and the actions of selfish micro-storage agents forms a globally stable feedback loop that converges to an efficient equilibrium. We further propose a market strategy that allows the supplier to reduce wholesale purchasing costs without increasing the uncertainty and variance for its aggregate consumer demand. Moreover, we empirically evaluate our mechanism (based on the UK grid data) and show that it yields savings of up to 16% in energy cost for consumers using storage devices with average capacity 10 kWh. Furthermore, we show that it is robust against extreme system changes.
Text
voice_etal_aaai2011.pdf
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Submitted date: 5 May 2011
Published date: 2011
Additional Information:
Event Dates: August 7–11, 2011
Venue - Dates:
AAAI-11: Twenty-Fifth Conference on Artificial Intelligence, San Francisco, United States, 2011-08-07 - 2011-08-11
Organisations:
Agents, Interactions & Complexity
Identifiers
Local EPrints ID: 272262
URI: http://eprints.soton.ac.uk/id/eprint/272262
PURE UUID: 897a9e1d-0983-45ae-8694-5270736928b9
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Date deposited: 09 May 2011 11:42
Last modified: 15 Mar 2024 03:22
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Contributors
Author:
Thomas Voice
Author:
Perukrishnen Vytelingum
Author:
Sarvapali Ramchurn
Author:
Alex Rogers
Author:
Nick Jennings
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