Abstract
Variability of renewable energy sources (RES) creates great challenges for their owners and power system operator. In recent years energy storage system is employed to mitigate the fluctuation of RESs. This paper addresses optimal decision-making of the wind-diesel-storage system in the day-ahead electricity market with a data-driven approach. The problem is formulated as MILP to maximize the profit. Uncertain parameters of the day-ahead electricity market and wind speed are forecasted by a deep bidirectional gated recurrent neural network (DBGRUNN). The proposed model is compared with LSTM, GRU, CNN, and Conv models and different evaluation criteria, as well as the mean absolute percentage error (MAPE), the root, mean square error (RMSE), and the mean absolute error (MAE) are used to measure accuracy. The presented model results on a Ferrol 1 Tide Gauge station database at the Spain pool market show that the proposed model performs better than the comparative models.
Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
