Flexible and sustainable scheduling of electric power grids: A dynamic line and transformer rating based approach under uncertainty condition

Abstract

Integration of renewable and sustainable energies into the electric power grids has affected planning and operation problems of the power system. Voltage and congestion as well as uncertainty issues are some challenges regarding this integration. Dynamic line and transformer ratings, known as DLR and DTR, are kinds of smart-grid technologies that can help alleviation of above-mentioned problems and perform a flexible scheduling for the power system. In this paper, the DLR and DTR have been simultaneously incorporated in optimal power flow (OPF) problem in wind-energy-integrated power systems considering the presence of uncertainties. The aim is to perform an optimal day-ahead scheduling of thermal and wind power units where the uncertainty of DLR and DTR as well as that of wind power generation are considered. The uncertainty scenarios are generated by Monte-Carlo method where k-means clustering algorithm is employed to reduce the number of scenarios within a scenario-based stochastic optimization. As the AC power flow and dynamic rating relations have non-convex nature, they have been convexified in order to formulate the optimization problem as a mixed-integer quadratically-constrained programming (MIQCP). The proposed approach is applied to IEEE-24-bus system in different experiments in GAMS environment. The simulations demonstrate efficiency of the conducted approach in reducing operating costs, minimizing load shedding cost, and maximum scheduling of wind power units as sustainable and clean energy resources.