How to Assess Uncertainty-Aware Frameworks for Power System Planning?

Abstract

Computational advances along with the profound impact of uncertainty on power system investments have motivated the creation of power system planning frameworks that handle long-run uncertainty, large number of alternative plans, and multiple objectives. Planning agencies seek guidance to assess such frameworks. This article addresses this need in two ways. First, we augment previously proposed criteria for assessing planning frameworks by including new criteria such as stakeholder acceptance to make the assessments more comprehensive, while enhancing the practical applicability of assessment criteria by offering criterion-specific themes and questions. Second, using the proposed criteria, we compare two widely used but fundamentally distinct frameworks: an ‘agree-on-plans’ framework, Robust Decision Making (RDM), and an ‘agree-on-assumptions’ framework, centered around Stochastic Programming (SP). By comparing for the first time head-to-head the two distinct frameworks for an electricity supply planning problem under uncertainties in Bangladesh, we conclude that RDM relies on a large number of simulations to provide ample information to decision makers and stakeholders, and to facilitate updating of subjective inputs. In contrast, SP is a highly dimensional optimization problem that identifies plans with relatively good probability-weighted performance in a single step, but even with computational advances remains subject to the curse of dimensionality.