Open AccessThis article is
- freely available
Optimization of Experimental Model Parameter Identification for Energy Storage Systems
Department of Industrial and Information Engineering, Second University of Naples, Via Roma 29, Aversa (CE) 81031, Italy
Department of Information Engineering Infrastructure and Sustainable Energy, University Mediterranea of Reggio Calabria, Via Graziella (Loc. Feo Vito), Reggio Calabria 89124, Italy
* Author to whom correspondence should be addressed.
Received: 28 March 2013; in revised form: 22 August 2013 / Accepted: 26 August 2013 / Published: 3 September 2013
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
Citations to this Article
Cite This Article
MDPI and ACS Style
Gallo, D.; Landi, C.; Luiso, M.; Morello, R. Optimization of Experimental Model Parameter Identification for Energy Storage Systems. Energies 2013, 6, 4572-4590.
Gallo D, Landi C, Luiso M, Morello R. Optimization of Experimental Model Parameter Identification for Energy Storage Systems. Energies. 2013; 6(9):4572-4590.
Gallo, Daniele; Landi, Carmine; Luiso, Mario; Morello, Rosario. 2013. "Optimization of Experimental Model Parameter Identification for Energy Storage Systems." Energies 6, no. 9: 4572-4590.