Abstract

Power system operators and planners are dealing both with the integration of unparalleled levels of variable renewable energy sources and deep uncertainties that originate from new technological developments, changing regulatory frameworks, unknown investment, operational costs of technologies, etc. An inadequate representation of the uncertainties may result in a substantial risk of deploying inflexible investment solutions incapable of adapting efficiently to evolving scenarios. In this context, this work studies the effects of increasing the granularity used to represent the long-term uncertainty by analysing its impact on the resulting optimal portfolios of new transmission lines, battery energy storage systems and pumped-hydro storage systems. The studies are conducted on an instance of the Australian power system described by the system operator for planning purposes, including four types of uncertainty granularity, namely deterministic representation, and 2-stage, 3-stage and 4stage stochastic representations. To address the computational challenges associated with the large mixedinteger linear stochastic problems, the different instances are reformulated using Dantzig-Wolfe decomposition, enabling the use of a column generation approach to solve the investment problem. The case study applications show substantial adjustments in the investment portfolios as uncertainty granularity changes, with a clear tendency to increase battery storage investment as uncertainty is better represented.