Distribution System Planning under Uncertainty: A Comparative Analysis of Decision-Making Approaches

摘要

In light of the significant uncertainty that distribution systems are facing with the uptake of distributed energy resources and ongoing technical and commercial transformations, planning emerges as a daunting task. Tools supporting decision making in uncertain environments become key to make cost-effective and risk-aware decisions. However, no clear comparison exists across methodologies whereby the relative cost and risk implications are assessed. On these premises, this paper proposes a comparative analysis of stochastic optimization, least-worst regret, and robust optimization, with specific application to distribution systems. After reviewing the features of each methodology, we introduce a planning optimization formulation, based on mixed integer linear programming, which allows a comparative assessment. We then test our modelling on a test case, demonstrating the cost and risk implications of the investment decisions selected by the different methodologies. The results provide novel insights on these methodologies, aiming to facilitate their adoption in future distribution system planning.