Abstract
The integration of high-level renewable energy, increased load demand, and the aging of the transmission network have all exacerbated the congestion of the transmission system. This requires transmission service providers to make full use of the existing transmission infrastructure by using cost-effective new transmission technologies, thereby taking full advantage of the inherent flexibility of the system. Dynamic thermal rating (DTR) is one of several technologies that can help address challenges with transmission operation, especially congestion management. Optimal transmission switching (OTS) and DTR technologies together provide flexible methods to enhance the performance of the power system and maximize the use of existing transmission system. In this study, a probabilistic multi-objective based congestion management procedure is proposed using OTS strategies considering the maximization of system reliability and minimization of the total generation cost. Additionally, the prevention of islanding is considered to ensure the feasibility of transmission switching status. The uncertainties associated with load demand and wind power generation are handled in this study using the points estimation method (PEM). The formulated optimization problem is solved using the multi-objective teacher learning based optimization (MOTLBO) algorithm. As the studied problem is a nonlinear constrained multi-objective optimization problem with conflicting objectives, the Pareto-optimality is used to find the set of optimal solutions. The best compromise solution among the Pareto set is chosen by a fuzzy decision-making approach. Several case studies are simulated on the modified IEEE RTS-96 system. The simulation results show that transmission system reconfiguration while considering the DTR leads to the enhancement of the system performance as compared to static line rating (SLR). For example, the generation cost has dropped by 6.78% when utilizing the DTR as compared to the SLR.
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