Colloquia

Summary

This thesis investigates the allocation of Mobile Energy Storage Systems (MESS) as a solution to enhance grid stability and optimize peak shaving within modern electricity distribution networks. Through the development of a bi-level optimization strategy, the research identifies optimal MESS placement and energy discharge schedules. The optimization framework integrates a Dynamic Hunter Leadership (DHL) heuristic algorithm and a Mixed-Integer Nonlinear Programming (MINLP) model to improve voltage stability and minimize operational costs and is tested across multiple scenarios. The results demonstrate that this hybrid approach can significantly reduce grid congestion, mitigate peak demand, and improve grid resilience. The financial analysis underscores the economic feasibility of MESS in dynamic grid environments. The findings highlight the potential of MESS to provide a scalable and flexible solution for modern energy challenges, with positive implications for both energy providers and grid operators.