An Adjustable Robust Optimization Approach for the Expansion Planning of a Virtual Power Plant

This paper proposes a novel approach based on adjustable robust optimization for the expansion planning of a virtual power plant (VPP) that participates in the energy electricity market. The VPP comprises conventional, renewable, and storage units, as well as flexible demands, and analyzes the possibility of building new conventional, renewable, and storage units with the aim of maximizing its profit. The uncertainty related to future production costs of the conventional generating units, future consumption levels of the flexible demands, and future energy market prices is modeled using confidence bounds and uncertainty budgets. The resulting model is formulated as a trilevel program with lower-level binary variables that is solved using a nested column-and-constraint generation algorithm. Results from a case study show the effective performance of the proposed approach.