Theoretical and Practical Foundations of 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 Agent-Based Micro-Storage in the Smart Grid. Journal of Artificial Intelligence Research, 42, 765-813.
- Published Version
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 \pounds 1.5B). In so doing, we also demonstrate how the system would eventually evolve to stable equilibria given different costs for batteries and different population mixes (of consumers with high and low consumption) and that the efficiency of the grid would indeed be improved (with significant reduction of peak demand across the grid) as long as the cost of storage is reasonably low or can be subsidised.
|Divisions:||Faculty of Physical Sciences and Engineering > Electronics and Computer Science > Agents, Interactions & Complexity
|Date Deposited:||25 Oct 2011 08:46|
|Last Modified:||28 May 2013 01:01|
|Contributors:||Vytelingum, Perukrishnen (Author)
Voice, Thomas (Author)
Ramchurn, Sarvapali (Author)
Rogers, Alex (Author)
Jennings, Nick (Author)
|Further Information:||Google Scholar|
|ISI Citation Count:||0|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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