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Stochastic Generation and Transmission Expansion Planning using Sample Average Approximation

The issue of modeling and incorporating uncertainty considering tractability in the planning of electrical power systems has become important for planners to maintain a reliable system. In this study, the generation and transmission expansion planning is performed considering the uncertainty in load and wind generation. The sample average approximation (SAA) method is used to obtain deterministic equivalent of stochastic optimization problems in a mixed integer linear programming (MILP) framework. The problem is formulated with a large number of independent samples, each small in size, to reduce computational effort. The results are presented by comparing them with investment decisions made using a single large sample size. It is observed that the presented approach can be used as an alternative in terms of sufficiently operationalizable to produce optimal investment decisions in mathematical programming.