Charge transport properties of organic conjugated polymers for photovoltaic applications

Charge transport in conjugated polymers as well as in bulk-heterojunction (BHJ) solar cells made of blends between conjugated polymers, as electron-donors (D), and fullerenes, as electron-acceptors (A), has been investigated. It is shown how charge carrier mobility of a series of anthracene-containing poly(p-phenylene-ethynylene)-alt-poly(p-phenylene-vinylene)s (AnE-PVs) is highly dependent on the lateral chain of the polymers, on a moderate variation of the macromolecular parameters (molecular weight and polydispersity), and on the processing conditions of the films. For the first time, the good ambipolar transport properties of this relevant class of conjugated polymers have been demonstrated, consistent with the high delocalization of both the frontier molecular orbitals. Charge transport is one of the key parameters in the operation of BHJ solar cells and depends both on charge carrier mobility in pristine materials and on the nanoscale morphology of the D/A blend, as proved by the results here reported. A straight correlation between hole mobility in pristine AnE-PVs and the fill factor of the related solar cells has been found. The great impact of charge transport for the performance of BHJ solar cells is clearly demonstrated by the results obtained on BHJ solar cells made of neat-C70, instead of the common soluble fullerene derivatives (PCBM or PC70BM). The investigation of neat-C70 solar cells was motivated by the extremely low cost of non-functionalized fullerenes, compared with that of their soluble derivatives (about one-tenth). For these cells, an improper morphology of the blend leads to a deterioration of charge carrier mobility, which, in turn, increases charge carrier recombination. Thanks to the appropriate choice of the donor component, solar cells made of neat-C70 exhibiting an efficiency of 4.22% have been realized, with an efficiency loss of just 12% with respect to the counterpart made with costly PC70BM.

Power flow forecasting and smart charging for electric vehicle charging systems with photovoltaic integration

An essential role in the global energy transition is attributed to Electric Vehicles (EVs) the energy for EV traction can be generated by renewable energy sources (RES), also at a local level through distributed power plants, such as photovoltaic (PV) systems. However, EV integration with electrical systems might not be straightforward. The intermittent RES, combined with the high and uncontrolled aggregate EV charging, require an evolution toward new planning and paradigms of energy systems. In this context, this work aims to provide a practical solution for EV charging integration in electrical systems with RES. A method for predicting the power required by an EV fleet at the charging hub (CH) is developed in this thesis. The proposed forecasting method considers the main parameters on which charging demand depends. The results of the EV charging forecasting method are deeply analyzed under different scenarios. To reduce the EV load intermittency, methods for managing the charging power of EVs are proposed. The main target was to provide Charging Management Systems (CMS) that modulate EV charging to optimize specific performance indicators such as system self-consumption, peak load reduction, and PV exploitation. Controlling the EV charging power to achieve specific optimization goals is also known as Smart Charging (SC). The proposed techniques are applied to real-world scenarios demonstrating performance improvements in using SC strategies. A viable alternative to maximize integration with intermittent RES generation is the integration of energy storage. Battery Energy Storage Systems (BESS) may be a buffer between peak load and RES production. A sizing algorithm for PV+BESS integration in EV charging hubs is provided. The sizing optimization aims to optimize the system's energy and economic performance. The results provide an overview of the optimal size that the PV+BESS plant should have to improve whole system performance in different scenarios.