A Probabilistic Approach for Scheduling of Wind-Energy-Integrated Power Systems Incorporating Dynamic Lines and Transformers Rating

In today's electric power systems, transmission congestion and integration of renewable-based power generations are challenging issues. To tackle with these problems, power system operators can make use of smart-grid technologies. Dynamic transformer rating (DTR) and dynamic line rating (DLR) are employed as two smart-grid technologies for stochastic day-ahead optimal power flow (OPF) under uncertainty condition. The uncertainty scenarios are generated using Monte-Carlo simulation where the K-means clustering algorithm is employed for the scenario reduction purpose. A convexified version of AC power flow and dynamic rating relations are employed to formulate the optimization problem as a mixed-integer quadratically-constrained programming (MIQCP). The conducted approach is applied to the IEEE-24-bus network in GAMS, and the simulation results are investigated to validate efficiency of the proposed method. The simulations show that the wind power curtailment and load shedding becomes zero in the case of utilizing dynamic ratings. Also, a total cost reduction of 41% is observed by the aid of DTR and DLR.