Unlocking the full value of battery storage: Fuse-constrained, multi-service stacking and peak shaving in a unified optimization framework

Battery energy storage systems enhance grid flexibility by enabling participation in frequency containment reserves (FCR), day-ahead (DA), and peak shaving (PS) markets—each with distinct operational and economic rules. Yet, operators face a key challenge: how to stack services without compromising reliability or lifespan? This study presents a unified mixed-integer linear programming framework for optimal multi-service stacking, rigorously integrating technical constraints — including real-world fuse limits and battery degradation — alongside market participation requirements. Uniquely, the model balances both droop-based and energy-based FCR participation, precise day-ahead market trading, and behind-the-meter cost management, all while tracking the interplay of physical and regulatory boundaries. The framework is tested using real industrial data from Sweden, under the coordinated rules of Svenska kraftnät. Results reveal that holistic co-optimization is not just a theoretical ideal but a practical economic lever: stacking services increases net profit by about 83% compared to the single-market strategy (DA). This highlights the need for a holistic approach that manages state of energy (SoE), degradation, and fuse limits. The analysis shows that moderate relaxations in fuse limits boost revenue, but benefits plateau, suggesting that reasonable sizing captures most economic gains without costly upgrades to fuses or grid infrastructure. • MILP model co-optimizes battery use in DA, FCR-D/N, and peak shaving markets. • Fuse constraints and peak shaving are integrated into a mathematically detailed MILP model. • Real asset-level data is used to reconstruct industrial load under operational limits. • Moderate fuse sizing is shown to maximize value, avoiding costly oversizing.