The objective of this study is to explore battery value stacking optimization. The analyses use real-world data of a battery energy storage system owned and operated by Arva, a local DSO in northern Norway. From a methodological point of view, a mixed integer linear programming model is developed with the objective of scheduling multiple battery services (namely energy arbitrage, peak shaving, islanding, and frequency regulation) with a set of technical and economic constraints. It can be observed from the computational results that energy arbitrage drives both revenue and battery degradation, especially in volatile markets, while frequency regulation maintains a steady use of the battery due to regulatory commitments. The proposed study integrates real-world and practical data with mathematical optimization for decision support, and therefore offers actionable insights for DSOs, contributing to bridging theory and practice in energy storage optimization.