Energy Scheduling of Wind-Storage Systems Using Stochastic and Robust Optimization

Energy storage systems (ESSs) is an emerging technology that enables increased and effective penetration of renewable energy sources into power systems. ESSs integrated in wind power plants can reduce power generation imbalances, occurring due to the deviation of day-ahead forecasted and actual wind generation. This work develops two-stage scenario-based stochastic and robust optimization schemes for the day-ahead energy scheduling of combined wind-storage systems, considering wind power uncertainty and the power balancing capability of the ESS. The set of scenarios used in the optimization schemes is appropriately selected from historical data of a real wind farm to closely match the day-ahead forecasted power production. Simulation results demonstrate the superiority of the stochastic and robust optimization schemes in terms of producer profits and power imbalances compared to the corresponding deterministic optimization scheme. The results also highlight that the two schemes offer a different trade-off between profits and power imbalances, making the stochastic and robust schemes more desirable for the producer and system operator, respectively.