A low-carbon oriented probabilistic approach for transmission expansion planning

Abstract

Following the deregulation of the power industry, transmission expansion planning (TEP) has become more complicated due to the presence of uncertainties and conflicting objectives in a market environment. Also, the growing concern on global warming highlights the importance of considering carbon pricing policies during TEP. In this paper, a probabilistic TEP approach is proposed with the integration of a chance constrained load curtailment index. The formulated dynamic programming problem is solved by a hybrid solution algorithm in an iterative process. The performance of our approach is demonstrated by case studies on a modified IEEE 14-bus system. Simulation results prove that our approach can provide network planners with comprehensive information regarding effects of uncertainties on TEP schemes, allowing them to adjust planning strategies based on their risk aversion levels or financial constraints.