Agent-Based Micro-Storage Management for the Smart Grid

Vytelingum, Perukrishnen, Voice, Thomas D., Ramchurn, Sarvapali D., Rogers, Alex and Jennings, Nicholas R. (2010) Agent-Based Micro-Storage Management for the Smart Grid. In, The Ninth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010) - Won the Best Paper Award, Toronto, Canada, 10 - 14 May 2010. , 39-46.


[img] PDF - Version of Record
Download (4Mb)


The use of energy storage devices in homes has been advocated as one of the main ways of saving energy and reducing the reliance on fossil fuels in the future Smart Grid. However, if micro-storage devices are all charged at the same time using power from the electricity grid, it means a higher demand and, hence, more generation capacity, more carbon emissions, and, in the worst case, breaking down the system due to over-demand. To alleviate such issues, in this paper, we present a novel agent-based micro-storage management technique that allows all (individually-owned) storage devices in the system to converge to profitable, efficient behaviour. Specifically, we provide a general framework within which to analyse the Nash equilibrium of an electricity grid and devise new agent-based storage learning strategies that adapt to market conditions. Taken altogether, our solution shows that, specifically, in the UK electricity market, it is possible to achieve savings of up to 13% on average for a consumer on his electricity bill with a storage device of 4 kWh. Moreover, we show that there exists an equilibrium where only 38% of UK households would own storage devices and where social welfare would be also maximised (with an overall annual savings of nearly GBP 1.5B at that equilibrium).

Item Type: Conference or Workshop Item (Paper)
Additional Information: Winner of the Best Paper Award Event Dates: May 10-14, 2010
Keywords: Energy, smart grid, agents, agent-based modelling, game-theory
Divisions : Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Agents, Interactions & Complexity
ePrint ID: 268360
Accepted Date and Publication Date:
10 May 2010Submitted
Date Deposited: 06 Jan 2010 22:59
Last Modified: 31 Mar 2016 14:16
Further Information:Google Scholar

Actions (login required)

View Item View Item

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics