Solar intermittency and storage

Courses of Study Solar intermittency and storage

Solar intermittency and storage

Curriculum 5

The intrinsic intermittent behavior of the solar source calls for a number of studies, which are useful in order to optimize the operations of photovoltaic (PV) plants.

On the one hand, the PhD candidate will study the integration of photovoltaic plants with energy storage systems (ESS) based on different technologies such as electrochemical, chemical, mechanical and thermal storage, which are used both for energy shift and peak shaving applications.

PV plants and ESS will be integrated in different environments such as nano and microgrids, energy communities and solar-powered e-vehicle charging stations with or without vehicle-to-everything (V2X) capabilities.

On the other hand, the PhD candidate will also tackle the intermittent behavior of the solar irradiance thanks to the opportunity offered by different forecasting tools. These, in fact, allow the forecasting of the solar irradiance and of the operating temperature, which play a key role in the prediction of the power produced by the PV plant.

The studies described above represent the background for the development of efficient battery management systems (BMS) and energy management systems (EMS), which are used for the optimization of the power and the energy fluxes, from both the economic and the environmental points of view. Another aspect to consider is the development of those ancillary services used to make the pairing of PV plants with ESS a reliable source of flexibility for the power system.

For these purposes, the PhD candidate will need to develop different forecasters for the prediction of the load, of the state of charge and health of the storage systems, of the voltage and the frequency of the grid, of the electricity price and of the carbon intensity.

The key competencies required of the candidates, in order to successfully complete this curriculum are: the design of power systems including PV generators and ESS, artificial intelligence-based techniques for the development of control and forecasting tools, optimization techniques and instruments for the assessment of the sustainability of the different developed systems, such as LCA (Life Cycle Assessment), LCOE (Levelized Cost Of Energy) and EROEI (Energy Return on Energy Invested).