A Two-Stage Stochastic Optimization Planning Framework to Decarbonize Deeply Electric Power Systems

In 2015, 195 countries signed the Paris Agreement under the United Nations Framework Convention on Climate Change. To achieve the ambitious greenhouse gas-reduction targets therein, the electric power sector must be transformed fundamentally. To this end, we develop a two-stage stochastic optimization model. The proposed model determines the optimal mix of generation and transmission capacity to build to serve future demands at least cost, while respecting technical constraints and climate-related considerations. The model uses a mix of AC and high-voltage DC transmission lines, conventional and renewable generation, and different types of energy-storage units to meet these objectives. Short- and long-term uncertainties are modeled using operating conditions and scenarios, respectively. We demonstrate the model using a case study that is based on the Texas power system, with 2050 as the target year of the analysis. We include explicit carbon-emissions constraints. Doing so allows us to examine the effect of carbon-reduction targets and deep decarbonization of electricity production on investment decisions. As expected, we find that thermal-dominated power systems must transition toward having a renewable-dominated generation mix