Dataset of "Sensitivity Analysis of Open-Loop Battery Scheduling to Forecast Uncertainty in Residential PV Systems"

This paper investigates the sensitivity of a day-ahead open-loop battery scheduling strategy to forecast errors in PV, load, and electricity prices. A mixed-integer linear programming (MILP) EMS is developed for a residential PV-battery-grid system and evaluated through annual hourly simulations. Forecast errors are introduced independently, and system performance is assessed using grid savings, net savings, equivalent battery cycles, and grid power exchange indicators. Four EMS configurations are considered to evaluate the influence of grid constraints and battery degradation costs. Results show that the EMS is largely insensitive to PV forecast errors and only moderately affected by load forecast errors, even at high levels of uncertainty. In contrast, electricity price forecast errors strongly affect economic performance and battery utilization. In degradation-unaware scenarios, increasing price forecast uncertainty reduces grid savings from approximately 22% to negative values and increases battery cycling from approximately 240 to 470 equivalent full cycles. Including battery degradation costs significantly reduces battery cycling and improves robustness. The findings highlight the dominant role of electricity price forecasting accuracy and the importance of degradation-aware metrics for realistic economic assessment.