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A greedy algorithm for the unforecasted energy dispatch problem with storage in Smart Grids

Giorgos Georgiadis (Institutionen för data- och informationsteknik, Nätverk och system, Datakommunikation och distribuerade system (Chalmers)) ; Marina Papatriantafilou (Institutionen för data- och informationsteknik, Nätverk och system, Datakommunikation och distribuerade system (Chalmers))
Göteborg : Chalmers University of Technology, 2012. - 18 s.
[Rapport]

Recent modernization efforts of the electrical grid led to the introduction of an additional communications and computations-based layer in the infrastructure, with the resulting new grid commonly referred to as smart grid. One of the aims of the smart grid is to facilitate the integration of renewable and distributed energy sources on a massive scale but these technologies bring benefits as well as challenges. Part of the challenge is due to intermittent energy sources, such as wind and solar, which generate energy at irregular intervals, leading to utilization problems for the grid. On the other hand, upcoming storage technologies, such as electrical cars, hold the potential to store and utilize this intermittent supply at a later time but bring challenges of their own, for example efficient storage utilization and intermittent energy demand. In this paper we present an algorithm that enables distributed solutions by utilizing efficiently any storage capabilities in order to mitigate the effect of unreliable or nonexistent demand forecasts. Drawing upon computer science methodology, we define and model the problem of unforecasted energy dispatch with storage as a scheduling problem of tasks on machines. We show that both storage and the time parameter inherent in the energy dispatch problem can be incorporated into a variant of the scheduling problem, leading to a novel modeling that can be used to study further the energy dispatch problem. In addition to presenting a simple but effective algorithm that solves the aforementioned problem, we also prove analytically that this is done in a near optimal way. Finally, we provide an extensive simulation study for a variety of scenarios, showing that the presented algorithm is highly competitive to methods that use forecasts and assume total knowledge about the demand requests.



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Denna post skapades 2012-12-03.
CPL Pubid: 166991