A column generation approach for solving generation expansion planning problems with high renewable energy penetration

Abstract

The high penetration of renewables envisaged for future power systems will significantly increasethe need for flexible operational measures and generation technologies, whose associated investmentdecisions must be properly planned in the long term. To achieve this, expansion models will need toincorporate unit commitment constraints, which can result in large scale MILP problems that require sig-nificant computational resources to be solved. In this context, this paper proposes a novel Dantzig–Wolfedecomposition and a column generation approach to reduce the computational burden and overcomeintractability. We demonstrate through multiple case studies that the proposed approach outperformsdirect application of commercial solvers, significantly reducing both computational times and memoryusage. Using the Chilean power system as a reference case, we also confirm and highlight the importanceof considering unit commitment constraints in generation expansion models.