Adjustable Decisions for Reducing the Price of Robustness of Capacity Expansion Planning

This paper proposes and implements robust optimization methodologies for making investment decisions in the capacity expansion planning (CEP) of power systems in an uncertain environment. Uncertainties of fuel prices, demand, and transmission capacity are captured in an uncertainty set. With adjustable robust optimization (ARO), we represent all the decision variables as affine functions of multiple uncertain data. This adjustability of decisions provides that the ARO solution has significant less price of robustness than in traditional robust optimization (RO). ARO models uncertainty in terms of parameter ranges, called "uncertainty sets." An attractive attribute of utilizing uncertainty sets is that they facilitate computational tractability when simulating scenarios with multiple uncertainties. We study the 40-year planning of a 5-region, 13-technology US energy portfolio. Results show that 1) by appropriately selecting the decision rules in ARO, the price of robustness can be significantly reduced while maintaining the same levels of robustness; and 2) the RO-based models maintain high levels of robustness even under operational conditions provided by data coming from larger sizes of the uncertainty sets.