With the growing application of green energy, the importance of effectively handling the volatile nature of these energy sources is also growing in order to ensure economic and operational viability. Accordingly, the main contribution of this work is to evaluate the revenue potential for wind parks with integrated storage systems in the day-ahead electricity markets using genetic algorithm. It is achieved by the concept of flexible charging–discharging of the Energy Storage System (ESS), taking advantage of the widespread electricity prices that are predicted using a feedforward-neuralnetwork-based forecasting algorithm. In addition, the reactive power restrictions posed by grid code that are to be followed by the wind park are also considered as one of the constraints. Moreover, the profit obtained with a Battery Energy Storage System (BESS) is compared with that of a Thermal Energy Storage System (TESS). The proposed method gave more profitable results when utilizing BESS for energy arbitrage in day-ahead electricity markets than with TESS. Moreover, the availability of ESS at wind park has reduced the wind power curtailment.