Theoretical and practical foundations of large-scale agent-based micro-storage in the smart grid

Vytelingum, Perukrishnen, Voice, Thomas, Ramchurn, Sarvapali, Rogers, Alex and Jennings, Nick (2011) Theoretical and practical foundations of large-scale agent-based micro-storage in the smart grid Journal of Artificial Intelligence Research, 42, pp. 765-813. (doi:10.1613/jair.3446).


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In this paper, we present a novel decentralised management technique that allows electricity micro-storage devices, deployed within individual homes as part of a smart electricity grid, to converge to profitable and efficient behaviours. Specifically, we propose the use of software agents, residing on the users' smart meters, to automate and optimise the charging cycle of micro-storage devices in the home to minimise its costs, and we present a study of both the theoretical underpinnings and the implications of a practical solution, of using software agents for such micro-storage management. First, by formalising the strategic choice each agent makes in deciding when to charge its battery, we develop a game-theoretic framework within which we can analyse the competitive equilibria of an electricity grid populated by such agents and hence predict the best consumption profile for that population given their battery properties and individual load profiles. Our framework also allows us to compute theoretical bounds on the amount of storage that will be adopted by the population. Second, to analyse the practical implications of micro-storage deployments in the grid, we present a novel algorithm that each agent can use to optimise its battery storage profile in order to minimise its owner's costs. This algorithm uses a learning strategy that allows it to adapt as the price of electricity changes in real-time, and we show that the adoption of these strategies results in the system converging to the theoretical equilibria. Finally, we empirically evaluate the adoption of our micro-storage management technique within a complex setting, based on the UK electricity market, where agents may have widely varying load profiles, battery types, and learning rates. In this case, our approach yields savings of up to 14% in energy cost for an average consumer using a storage device with a capacity of less than 4.5 kWh and up to a 7% reduction in carbon emissions resulting from electricity generation (with only domestic consumers adopting micro-storage and, commercial and industrial consumers not changing their demand). Moreover, corroborating our theoretical bound, an equilibrium is shown to exist where no more than 48% of households would wish to own storage devices and where social welfare would also be improved (yielding overall annual savings of nearly £1.5B).

Item Type: Article
Digital Object Identifier (DOI): doi:10.1613/jair.3446
Additional Information: AAMAS 2010 iRobot Best Paper Award
Organisations: Agents, Interactions & Complexity
ePrint ID: 272961
Date :
Date Event
Date Deposited: 25 Oct 2011 08:46
Last Modified: 17 Apr 2017 17:36
Further Information:Google Scholar

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