A probability -based Generation Capacity Expansion Model for planning Electric Power Systems

Resource adequacy evaluation and the decisionmaking process of generation capacity expansion (GCE) are closely related and important for power system planning. Without improved representations of the formulation of how the adequacy metrics will be evaluated, the planning and investment models are limited in their ability to assess power system performance under high intermittent renewable penetration. This paper proposes a probabilistic GCE model with an analytical formulation of adequacy indices evaluation to combine both probabilistic and optimization techniques for optimizing installed generation reserve required to meet ongoing resource adequacy standards. The analytical formulation relating adequacy indices is constructed based on the cumulants and Gram-Charlier expansion. The proposed method is validated by a case study. The proposed model possesses high computation efficiency with desirable solution accuracy, and thus it can significantly enhance the practicability of the GCE planning model in real power systems.