Colloquia

Summary

In the pursuit of climate neutrality and sustainability, the EU has established multiple goals to reduce environmental impact across various sectors. The manufacturing industry, a major contributor to environmental pollution, is part of this transformation. By adopting sustainable practices, the manufacturing sector can contribute to the EU's climate objectives. A crucial pillar of sustainable manufacturing is the integration of renewable energy sources as a source of energy into the production process. Particularly, on-site solar energy generation stands out as one of the most feasible options. However, it is important to manage the intermittent nature of PV energy generation. A most promising way is to incorporate batteries into PV systems, to effectively address the intermittent and non-dispatchable PV generation. Moreover, with the grid experiencing considerable congestion issues due to the increased penetration of renewable energy sources, constraints on the amount of imported or exported electricity imply the necessity of storage solutions to store the excess solar energy that would otherwise be curtailed or wasted.

Determining the optimal battery size for a PV system can be a challenge due to multiple factors, including the intermittent nature of renewable sources of energy, the fluctuations of demand, the continuously evolving regulatory framework and the trade-offs between the different objectives of the system.

This master thesis addresses the challenge of battery sizing optimization for grid-tied PV systems in the manufacturing context. The research is done for the Fraunhofer Innovation Platform for Advanced Manufacturing with a PV system of 36.9 kWp installed on the roof of the building and the generated energy is utilized to supply power to a 3D printer. Firstly, the optimization of the battery size is realized using a MILP model, taking into consideration four different objectives, namely cost minimization, CO2 emissions minimization, self-sufficiency and self-consumption maximization. Various scenarios are analyzed to account for different financial contexts and regulatory constraints. Due to the trade-offs among these objectives, a co-optimization analysis is also conducted. Furthermore, the CO2 emissions of the resulting optimal systems are calculated and a financial analysis is performed to assess the financial implications of the optimized systems. Lastly, the system's data are projected over the expected lifetime of the battery, considering changes in the performance of the PV-battery system, consumption profile, costs and regulatory framework, to analyze potential variations in the resulting optimal battery size.