In recent years, the yearly share of electricity generated in the Netherlands from renewable sources such as solar and wind has increased significantly, reaching 42% in 2023, and is projected to rise to 70% by 2030. However, production from these sources is highly weather-dependent, unpredictable, and often misaligned with typical periods of peak electricity demand. This mismatch leads to price volatility in electricity markets, emphasizing the importance of a reliable trading strategy for assets with flexible production or demand. Solar panels are partially flexible assets, as their production can be curtailed. However, their output is weather-dependent and cannot be predicted with complete accuracy. This creates a challenge in determining the trade volume in the day-ahead market, resulting in the highest profit. Trading conservatively in the day-ahead market reduces revenue but minimizes imbalance volumes through the possibility of curtailment. On the other hand, trading too much can result in unavoidable imbalances when actual production falls short. Existing literature focuses on minimizing imbalance volumes as extreme prices, high volatility, and lower average prices than day-ahead prices characterize the imbalance market. In these works, it is typically assumed that the real-time imbalance price is unavailable. However, real-time imbalance price predictions are available in this research, enabling optimal real-time decision-making. This provides the opportunity to profit from high imbalance prices while avoiding negative prices. In this study, a coordinated bidding strategy optimizes day-ahead bids, balancing revenue maximization and risk minimization as both the production and imbalance price are uncertain. The primary objective is to explore methods for incorporating uncertain imbalance prices into day-ahead optimization. Various methods from the literature are compared, and a novel decision-focused approach is introduced. Combined with solar generation forecasts, state-of-the-art day-ahead price predictions, and optimization models, monthly revenues are simulated using \ac{EMS} software. Results show that modeling uncertain imbalance prices using historical scenarios achieves the highest and most consistent revenues, especially when combined with \ac{CVaR} optimization. The novel decision-focused approach also performs among the best models, delivering consistently high revenues. Adding battery storage to the solar panels yields similar results, and further revenue increases are possible with improved solar forecasting. This highlights an important direction for future research.