The rise in renewable energy sources causes more imbalances in the power grid. These imbalances are handled on the secondary control reserve (SCR) market. The prices on the market are currently predominately determined by hydropower plants, making the market unattractive to potential market players. This thesis explores the development of a bidding strategy for these new players to enter the Swiss secondary control reserve (SCR) market. This is a sensitive matter, since bidding too high would result in the bid not being accepted, and bidding too low would mean a player could have earned more money. Two products are traded on the SCR market: negative control reserve (NCR), activated in case of an overbalance of the grid, and positive control reserve (PCR), activated in case of an underbalance of the grid. To develop a bidding strategy, the NCR and PCR bidding prices were modelled by using an ARIMA model to forecast the next week’s maximum bid. The order of the model was selected by minimising the Akaike information criterion and the parameters were estimated by maximising the likelihood function. An ARIMA(1,2,1) model provided the lowest AIC score for both the NCR and PCR data. The accuracy of the models was tested by examining the mean absolute error (MAE), the root mean squared error (RMSE) and the bias. To test the performance of the model on the SCR market, two additional metrics were introduced: the percentage of the bids accepted (PAB), and the percentage of the total potential revenue earned (PMR). Although the MAE, RMSE and bias of the ARIMA(1,2,1) models were low, the PAB and PMR were low as well. This is because both models tended to estimate the forecasts higher than the actual maximum prices, resulting in the bids not being accepted. By shifting the model down to the lower bound of the 95% one-step confidence interval, the PAB and PMR were more than doubled. Therefore, the forecasts of the shifted ARIMA(1,2,1) models, generated the best bidding price for the upcoming week.