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Optimal storage scheduling using Markov decision processes

Authors: GRILLO, SAMUELE; PIEVATOLO, ANTONIO; TIRONI, ENRICO;

Optimal storage scheduling using Markov decision processes

Abstract

The paper presents a method based on Markov decision processes to optimally schedule energy storage devices in power distribution networks with renewable generation. The time series of renewable generation is modeled as a Markov chain which allows for the implementation of a stochastic dynamic programming algorithm. The output of this algorithm is an optimal scheduling policy for the storage device achieving the minimization of an objective function including cost of energy and network losses. Besides this, other properties, such as energy storage placement and size, can be assessed and compared in optimized systems with different layouts.

Country
Italy
Keywords

Energy storage; Markov processes; renewable generation; stochastic dynamic programming, Energy storage, Markov processes, renewable generation, stochastic dynamic programming, elettrici

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    60
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
60
Top 1%
Top 10%
Top 10%
Green