Management and modelling of battery storage systems in microGrids and virtual power plants

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.