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).

Smart meters are intended to allow suppliers electricity network technologies, collectively called to access detailed energy consumption data and, more the smart grid (US Department Of Energy 2003; Galvin importantly, provide network information, such as real-time and Yeager 2008; UK Department of Energy and Climate pricing (RTP) signals, to consumers in an attempt to better Change 2009). A major component of this future vision is control or reduce demand when electricity is expensive that of energy storage. In particular, there is potential seen or carbon intensive on the grid (Hammerstrom et al. 2008; in the widespread adoption of small scale consumer storage Smith 2010). Accordingly, we envisage that micro-storage devices (i.e., micro-storage), which would allow consumers will be controlled by autonomous software agents that will to store electricity when demand is low, in order for react to RTP signals to minimise their owner's costs (i.e., it to be used during peak loads (Bathurst and Strbac 2003; they are self-interested). In this vein, we note our recent Ramchurn et al. 2011a; Vytelingum et al. 2010). This technology work (Vytelingum et al. 2010) in which we showed that, has the added advantage that it requires no significant when acting purely selfishly, large numbers of micro-storage change in how home appliances are used, and thus allows agents can cause instability in the aggregate demand profile.