Commercial Virtual Power Plants (CVPPs) have recently emerged as one of the most promising solutions for enabling intermittent renewable energy generation sources to efficiently trade the energy they generate in the electricity market. In this study, we develop several optimization and forecasting methods, and apply them to model the operation of multiple renewable generators across Scotland, trading energy as a single CVPP. The aim of the techniques developed is to optimize the scheduling of the CVPP, such as to maximize revenues and reduce the penalties resulting from forecasting errors, while considering operational and market constraints, such as variable costs, ramping rates, start-up costs, day-ahead and imbalance prices. The practical application is based on a case study of operational renewable energy plants in Scotland, and optimizes the CVPP operation for 3 months in winter and summer of 2017, respectively. Renewable generation output, day-ahead prices and imbalance prices are obtained from historical data for the same year. The numerical results show a profit increase of around 12% for the CVPP compared to standalone operation of renewable plants. This increase is observed for different market and imbalance settlement strategies.