2026-09-22T07:41:06Zhttps://iris.unica.it/oai/requestoai:iris.unica.it:11584/2667492022-10-20T09:22:42Zcom_11584_207615com_11584_111066col_11584_265854
Management and modelling of battery storage systems in
microGrids and virtual power plants
MUSIO, MAURA
Vehicle-to-Grid
concentrating photovoltaic systems
eletric vehicles
energy management strategies for Smart Grids
fotovoltaico a concentrazione
lithium batteries
micro reti
microgrids
sistemi di accumulo
sodium-nickel chloride batteries
strategie di gestione per reti intelligenti
veicoli elettrici
virtual power plants
Settore ING-IND/32 - Convertitori, Macchine e Azionamenti Elettrici
In the novel smart grid configuration of power networks, Energy Storage Systems
(ESSs) are emerging as one of the most effective and practical solutions to improve
the stability, reliability and security of electricity power grids, especially in presence
of high penetration of intermittent Renewable Energy Sources (RESs).
This PhD dissertation proposes a number of approaches in order to deal with
some typical issues of future active power systems, including optimal ESS sizing
and modelling problems, power
ows management strategies and minimisation of
investment and operating costs. In particular, in the first part of the Thesis several
algorithms and methodologies for the management of microgrids and Virtual Power
Plants, integrating RES generators and battery ESSs, are proposed and analysed
for four cases of study, aimed at highlighting the potentialities of integrating ESSs
in different smart grid architectures. The management strategies here presented are
specifically based on rule-based and optimal management approaches. The promising
results obtained in the energy management of power systems have highlighted
the importance of reliable component models in the implementation of the control
strategies. In fact, the performance of the energy management approach is only as
accurate as the data provided by models, batteries being the most challenging element
in the presented cases of study. Therefore, in the second part of this Thesis,
the issues in modelling battery technologies are addressed, particularly referring to
Lithium-Iron Phosphate (LFP) and Sodium-Nickel Chloride (SNB) systems. In the
first case, a simplified and unified model of lithium batteries is proposed for the
accurate prediction of charging processes evolution in EV applications, based on the
experimental tests on a 2.3 Ah LFP battery. Finally, a dynamic electrical modelling
is presented for a high temperature Sodium-Nickel Chloride battery. The proposed
modelling is developed from an extensive experimental testing and characterisation
of a commercial 23.5 kWh SNB, and is validated using a measured current-voltage
profile, triggering the whole battery operative range.
2016-03-30
info:eu-repo/semantics/doctoralThesis
http://hdl.handle.net/11584/266749
eng
numberofpages:165
info:eu-repo/semantics/openAccess
Università degli Studi di Cagliari
license:Non specificato