Stochastic chance constrained transmission expansion decisions for different investment budgets

This paper proposes a chance constrained programming based method for transmission expansion planning (TEP) that takes into account uncertainties in load demand and wind power. These uncertainties are represented by two random variables in the optimization process and they are incorporated into TEP model using probabilistic constraints. The objective of the proposed method is to minimize the investment, generation, and load curtailment costs while introducing risk factors associated with the probability of constraints. The impact of load and wind uncertainties on network expansion decisions is analyzed under different investment budgets. The optimization results show that the proposed method can be used by power system planners to explore the possibility of transmission decisions under different uncertainty scenarios.