This master thesis was carried out in collaboration with the company Starke Energy with the purpose of creating a model to be used when sizing projects according to each client preferences. The objective of the model was to maximize the revenues of each individual project by optimizing the battery capacity depending on different parameters such as: the grid connection size, the availability of PV panels and its total capacity and the consumption of each building. The objective of this thesis is also to demonstrate the importance of such models for companies operating in the same area as Starke - virtual power plants performing services such as frequency containment - and to show one possible method to increase operations efficiency when delivering the project specifications to clients - to be a link between the energy storage sizing algorithms of companies and its clients inputs. The optimization model takes as input data provided by Starke Energy company, related to the best bidding strategies to participate in electricity markets. Then, it uses client ́s data to evaluate the best possible solutions of a battery+pv+grid system. It does so by comparing several combinations of sce
narios taking into account the current grid connection, the PV status - if it is already installed or not - and the different battery sizes available. In the end, the model outputs three different solutions: a minimizing one where the grid
size is downgraded, a maximizing one where the connection is leveled up and a scenario where there are no modifications on the grid capacity. Each solution will show what is the design and size of the system, the investment needed and how it is divided, the different revenues sources and finally the ROI and payback periods. All of these parameters are demonstrated for 3 case studies. The first case study demonstrates that the expected outcome is not actually the best one, the second explains how negative results can turn into positive ones and the third shows one example for cases in which the grid size is already maximum. All in all, the three case studies are, most importantly, an illustration of the flexibility of the model since it can adapt to several different scenarios. In conclusion, although the model could be further developed to return more parameters or to adapt to more situations, its results were confirmed by specialists in the field and most importantly, it has helped the company to increase its efficiency by being able to connect its energy storage sizing algorithm with all of its clients inputs and deliver reliable and accurate information about each project specifications in a matter of days instead of weeks
