Mitigating imbalances from wind power by using an agent-based matching mechanism

The intermittent nature of wind flow hinders the effective prediction of wind power generation, causing imbalances between the estimated and the actual values. Therefore, large-scale integration of wind generation is limited by its consequences to the whole system. This paper focuses on a two-step approach to mitigate the imbalance caused by the integration of wind energy sources in the electric grid. First, an artificial neural network based forecasting tool will be developed for short-term prediction of wind power generation that aims to quantify power imbalance. In the second step, an agent-aggregator model is used to mitigate the aforementioned power imbalance by committed flexible distributed energy resources. A case study based on data from Swedish island Gotland is used for modeling and simulations.