Enhancing Power Flow with Dynamic Line Rating Effect Using Model Predictive Control

Abstract

Due to the high penetration of renewable energy "solar and wind", the overhead transmission line (OHTL) overloading problem arises, consequently the uncertainty increases. This makes it difficult to operate the energy system without violating the OHTL ampacity rating. To solve this problem, new transmission lines are created, which are costly and require considerable time. In the power system operation, the OHTL ampacity rating is maintained constant for all year, to ensure the safety and security of operation. However, OHTL is not used efficiently most of the time. Therefore, the dynamic line rating (DLR) is presented to ensure better utilization of available assets and to postpone the need to build new infrastructure. This paper introduces the power flow by considering the DLR characteristics of OHTL based on changes in the line ampacity due to the high penetration of intermittent sources. This paper provides a predictive control model to combine the effect of the expected DLR with the energy flow. The artificial neural network (ANN) is introduced for forecasting the DLR as a function of the conductor temperature, ambient temperature, wind speed, and solar radiation. Model predictive control (MPC) is used to control energy flow and DRL is used as a constraint in the control technique. The main features of MPC are the ability to reduce the disturbance and simple controls.