This paper investigates the impact of price forecast uncertainty on the economic performance of a battery energy storage system (BESS) participating simultaneously in the Nordic day-ahead electricity and frequency containment reserve markets; FCR-N, FCR-D-Up, and FCR-D-Down. The objective is to quantify how forecast errors in terms of bias deviation and uncertainty dispersion affect operational decision-making, market participation strategies, and expected profitability.

To this end, a stochastic optimization framework is developed to model the operation of a BESS participating in multiple electricity and reserve markets, considering the battery degradation cost. The price uncertainty is represented through a scenario-based approach, where forecast bias and variance of day-ahead electricity and reserve market prices are incorporated in generating the price scenarios.

The stochastic optimization problem is formulated to maximize the expected profit of the BESS across all scenarios and all markets while respecting operational and technical constraints, including BESS power balance, bid size and power bounds, and ENTSO-e power constraints. The model is solved on the daily basis, with hourly time step, over an annual horizon, and the resulting optimal dispatch schedules are evaluated under realized market prices to quantify the economic impact of forecast errors.

To evaluate the proposed framework, a case study was conducted on an 80 kWh BESS system using 2025 day-ahead electricity prices for the SE3 bidding zone from Nord Pool and FCR market data provided by Svenska Kraftnät. The results from the case study provide several important insights. First, forecast bias has a larger impact on profitability than variance. A bias of 20% yields a profit gap of approximately 14.7% relative to the perfect-foresight benchmark, compared to 1.0% for variance alone at its highest tested level of 20%. Therefore, while the system is relatively robust to symmetric uncertainty, even relatively low systematic errors in price forecasts lead to significant profit loss. Second, the interaction between bias and variance is non-negligible, as higher uncertainty levels (in variance) amplifies the negative effects of biased forecasts. Finally, results of the case study demonstrate that the day-ahead market price forecast is the dominant driver of system performance compared to other market prices. Among reserve markets, uncertainty in FCR-N prices has the strongest influence on operational outcomes.

These findings highlight the critical importance of forecast accuracy for the economic operation of multi-market energy systems. The study shows that reducing forecast bias yields greater economic benefits than lowering variance. The work is based on an original computational analysis using real market data and contributes to ongoing research in ancillary services markets, and decision-making under uncertainty. The proposed framework will benefit market participants, specifically BESS owners, in allocating forecasting focuses and formulating profitable and robust operational strategies.