Ice storage systems can be used as an efficient cooling source during summer, as well as a heat source for heat pumps during winter. The non-linear behavior of the heat exchange process in storage makes formulations for optimizing the design and operation of these technologies complex. In this work, we propose a quadratically-constrained mixed-integer programming formulation, that can capture the latent and sensible behavior of the storage and its impact on the delivery of heating and cooling. A building demonstrator integrating an ice storage device was used as a case study. Monitoring data were used to validate the simplified ice storage model employed in the optimization. Results showed that the most common optimal storage cycle requires freezing the water during late winter and when the air temperature falls below 0 °C. Increasing the storage volume increases both storage efficiency and the amount of free cooling available during summer. For these reasons, economies of scale can make larger systems more competitive than smaller ones. Storage size and thermal insulation level affect the duration of the charging and discharging phases. Thermal insulation improves seasonal efficiency and free cooling significantly. A higher CO₂ emissions price does not yield significant benefits in terms of emissions reductions. High investment costs and the seasonal variation in CO₂ intensity of electricity reduce the economic and environmental competitiveness of long-term ice storage systems, respectively.