Achieving Emission-Reduction Goals: Multi-Period Power-System Expansion under Short-Term Operational Uncertainty

Stochastic adaptive robust optimization is capable of handling short-term uncertainties in demand and variable renewable-energy sources that affect investment generation transmission capacity. We build on this setting by considering a multi-year horizon for finding the optimal plan capacity expansion while reducing greenhouse gas emissions. In addition, we incorporate multiple hours power-system operations to capture hydropower flexibility requirements utilizing such as wind solar power. To improve computational performance existing exact methods problem, employ Benders decomposition solve mixed-integer quadratic programming problem avoid computationally expensive big-M linearizations. The results realistic case study Nordic Baltic region indicate which investments transmission, power, flexible are required Through out-of-sample experiments, show stochastic model leads lower expected costs than under increasingly stringent environmental considerations.