The problem of incorporating non-dispatchable energy sources such as solar energy into electric utility capacity expansion programs is as yet unsolved. This thesis develops methods to incorporate solar energy as a decision variable into capacity planning and capacity expansion planning algorithms. The model is based on variable or intermittent availability of solar energy. For capacity planning, certain convexity properties are established which lead to an efficient decomposition process using a Newton-type search method. For the capacity expansion planning problem, a modification of Benders' Decomposition is applied, which breaks up the problem into a master program containing the solar decision variable, and a subproblem which involves an expansion problem in conventional equipment types. The primal solution to the subproblem is found with existing algorithms, the dual solution can be obtained from the primal solution employing a network interpretation of the problem. This analysis leads to an efficient tangential approximation method. Illustrative examples, computational experience and further generalizations are also provided.