Optimal Scheduling for Energy Storage Systems in Distribution Networks

Miquel Escoto, Mario Montagud, Noemi González, Alejandro Belinchón, Adriana Valentina Trujillo, Julián Romero, Julio César Díaz-Cabrera, Marta Pellicer García and Alfredo Quijano López
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Miquel Escoto: Instituto Tecnológico de la Energía (ITE), 46980 Paterna, Valencia, Spain
Mario Montagud: Instituto Tecnológico de la Energía (ITE), 46980 Paterna, Valencia, Spain
Noemi González: Instituto Tecnológico de la Energía (ITE), 46980 Paterna, Valencia, Spain
Alejandro Belinchón: Instituto Tecnológico de la Energía (ITE), 46980 Paterna, Valencia, Spain
Adriana Valentina Trujillo: Instituto Tecnológico de la Energía (ITE), 46980 Paterna, Valencia, Spain
Julián Romero: Instituto Tecnológico de la Energía (ITE), 46980 Paterna, Valencia, Spain
Julio César Díaz-Cabrera: Instituto Tecnológico de la Energía (ITE), 46980 Paterna, Valencia, Spain
Marta Pellicer García: Instituto Tecnológico de la Energía (ITE), 46980 Paterna, Valencia, Spain
Alfredo Quijano López: ITE, Universitat Politècnica de València, 46022 Valencia, Spain

Energies, 2020, vol. 13, issue 15, 1-12

Abstract: Distributed energy storage may play a key role in the operation of future low-carbon power systems as they can help to facilitate the provision of the required flexibility to cope with the intermittency and volatility featured by renewable generation. Within this context, this paper addresses an optimization methodology that will allow managing distributed storage systems of different technology and characteristics in a specific distribution network, taking into account not only the technical aspects of the network and the storage systems but also the uncertainties linked to demand and renewable energy variability. The implementation of the proposed methodology will allow facilitating the integration of energy storage systems within future smart grids. This paper’s results demonstrate numerically the good performance of the developed methodology.

Keywords: energy storage system management; demand and generation forecast; optimal scheduling of distributed energy storage; distribution network modelling and simulation; optimization models (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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