Optimization of Experimental Model Parameter Identification for Energy Storage Systems

Daniele Gallo, Carmine Landi, Mario Luiso and Rosario Morello
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Daniele Gallo: Department of Industrial and Information Engineering, Second University of Naples, Via Roma 29, Aversa (CE) 81031, Italy
Carmine Landi: Department of Industrial and Information Engineering, Second University of Naples, Via Roma 29, Aversa (CE) 81031, Italy
Mario Luiso: Department of Industrial and Information Engineering, Second University of Naples, Via Roma 29, Aversa (CE) 81031, Italy
Rosario Morello: Department of Information Engineering Infrastructure and Sustainable Energy, University Mediterranea of Reggio Calabria, Via Graziella (Loc. Feo Vito), Reggio Calabria 89124, Italy

Energies, 2013, vol. 6, issue 9, 1-19

Abstract: The smart grid approach is envisioned to take advantage of all available modern technologies in transforming the current power system to provide benefits to all stakeholders in the fields of efficient energy utilisation and of wide integration of renewable sources. Energy storage systems could help to solve some issues that stem from renewable energy usage in terms of stabilizing the intermittent energy production, power quality and power peak mitigation. With the integration of energy storage systems into the smart grids, their accurate modeling becomes a necessity, in order to gain robust real-time control on the network, in terms of stability and energy supply forecasting. In this framework, this paper proposes a procedure to identify the values of the battery model parameters in order to best fit experimental data and integrate it, along with models of energy sources and electrical loads, in a complete framework which represents a real time smart grid management system. The proposed method is based on a hybrid optimisation technique, which makes combined use of a stochastic and a deterministic algorithm, with low computational burden and can therefore be repeated over time in order to account for parameter variations due to the battery’s age and usage.

Keywords: smart grid; power and energy measurement; energy storage; mathematical battery model; optimisation techniques (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: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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