The integration of renewable generation into power system brings challenges and threats for the power system operation and planning. Transmission congestion is one of the most intricate problems. Congestion cost links the short-term system operation and long-term network planning. But few literature study the congestion cost over long term. The main innovation of this paper is to employ statistical probability of wind and load from real data to calculate congestion cost over a long period. The high accuracy and fast speed of the proposed calculation method are verified through a case study. The main contribution of this paper is the introduction of a typical congestion cost increasing curve after exploring the impacts of wind generation capacity, transmission capacity and peak load on congestion cost. The simulation results show that, with increasing wind generation capacity connected, the congestion cost increasing curve is divided into three periods - constant, exponential increasing and saturation. The dividing points between different periods are determined by transmission capacity and wind/load characteristics. Furthermore, the constant values in constant and saturation periods are determined by different electricity prices and the transmission capacity shortage. The research findings contribute to congestion cost forecasting and renewable generation deployment.
|Title of host publication||Power Engineering Conference (UPEC), 2013 48th International Universities|
|Publication status||Published - 2013|
|Event||2013 48th International Universities' Power Engineering Conference, UPEC 2013 - Dublin , Ireland|
Duration: 2 Sep 2013 → 5 Sep 2013
|Conference||2013 48th International Universities' Power Engineering Conference, UPEC 2013|
|Period||2/09/13 → 5/09/13|
Li, J., Zhou, L., & Li, F. (2013). Statistical probability based transmission congestion cost increasing tendency analysis. In Power Engineering Conference (UPEC), 2013 48th International Universities (pp. 1-6).  IEEE. https://doi.org/10.1109/UPEC.2013.6714911