Multi Objective Optimization for Intra Day Scheduling of Residential PV Battery Systems The increasing integration of renewable energy into the electricity grid poses stability challenges due to their inherent volatility. Residential PV-battery systems can help address these issues by dynamically responding to forecast deviations and uncertainties. This thesis builds upon a novel stochastic optimization approach, where the key innovation lies in the asymmetric allocation of uncertainty between the battery system and the power grid, enabled through mixed random variables. The proposed method extends this approach by additionally incorporating an intraday scheduling framework. Based on probabilistic forecasts of the combined production and consumption of the household – which will be referred to as prosumption – the original model provides stochastic schedules for both the grid and battery system over a 24-hour horizon. The intraday approach then consecutively solves optimization problems that update these schedules, all while considering new real time measurements and forecasts. To analyse the interplay of the conflicting objectives of promoting self-sufficiency while ensuring grid stability, various multi objective optimization (MOO) techniques will be implemented. The MOO setting will be combined with sequential decision-making techniques to properly model the consecutive intra-day approach. The effectiveness of this approach will be assessed using sample forecast scenarios.