Achieving the ambitious climate change mitigation objectives set by governments worldwide is bound to lead to unprecedented amounts of network investment to accommodate low-carbon sources of energy. Beyond investing in conventional transmission lines, new technologies such as energy storage and Flexible AC Transmission devices (FACTs) can improve operational flexibility and assist with the cost-effective integration of renewables. In addition, planners are facing increasing uncertainty with respect to the future energy mix, electrification of the transport and heating sectors and others. Traditionally, such planning problems have been cast as large Mixed Integer Linear Programming (MILP) problem. However, their large size, typically entailing millions of decision variables, can render their solution problematic.
In this talk we present an overview of state-of-the art decomposition techniques for solving large MILP planning problems under uncertainty. We focus on the use of nested decomposition schemes and present efficient re-formulations and example case studies. Further ideas related to the use of a robust decision criterion (min-max regret) and modelling decision-dependent uncertainties will also be presented.
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