Game-Theoretic Approach for Multi-Criteria Optimization of Community Energy Systems

The idea behind this thesis stems from the need to optimize the sizing of collective self-consumption systems and renewable energy communities. By considering the increasing importance of end consumers and their contribution to climate neutrality goals, these initiatives are emerging as promising solutions. However, due to their complexity and the presence of multiple conflicting objectives, it is necessary to develop suitable optimization approaches. In this context, the thesis proposes a Multi-Objective Optimization Problem based on Game Theory. Traditional Multi-Objective methods are inadequate for effectively managing the numerous objective functions involved (going from multi to many objectives), whereas the proposed method demonstrates greater capability in handling a higher number of objectives. This approach goes beyond the simple simulation of different configurations to select the best one for the analyzed case, instead, considering the scenario as a game between various players, allows the direct identification of optimal solutions. Moreover, this optimization methodology can be applied not only to energy systems, but also to numerous other contexts. In light of this, after a brief introduction to renewable energy communities and collective self-consumption, the proposed optimization methods are examined in detail. Different procedures have then been implemented in Python and tested on benchmark multi-objective problems. Afterwards, an optimization case study is performed on a collective self-consumption system using a dedicated simulation tool adapted to the specific context. Finally, the results obtained through the multi-objective optimization approach based on Game Theory are presented and also compared, when possible, with those derived from traditional optimization methods.