A chance constrained programming approach to transmission system expansion planning

The transmission system plays a critical role in providing access to all participants in a competitive electricity market for supply and delivery of electric power. Deregulation of the power industry brings many new challenges to the transmission system optimal planning problem, such as how to handle uncertain factors concerning the locations and capacities of new power plants as well as the demand growth for the transmission system planning period studied. Although the transmission system optimal planning problem has been extensively studied, available standard optimization models and methods cannot well solve this problem for the competitive electricity market environment with many uncertain factors involved. Given this background, a new method for the optimal transmission system expansion planning based on chance constrained programming is presented in this paper with several uncertain factors such as the locations and capacities of new power plants as well as demand growth well taken into account. A stochastic optimization model is first formulated under the presumption that the locations and capacities of new power plants and future load demands could be modeled as specified probability distributions. A method is then presented for solving the optimization problem using the well-known Monte Carlo simulation method and genetic algorithm. Finally, a numerical example is served for illustrating the essential features of the developed model and method.