A robust optimization method for planning regional-scale electric power systems and managing carbon dioxide

Abstract

The uncertainties that are inherent in the energy systems planning process and complexities interaction among various uncertain parameters are challenging managers and decision makers. In this study, a robust interval-stochastic optimization (RISO) method is developed for planning energy systems and trading carbon dioxide (CO2), through incorporating interval-parameter programming (IPP) within a robust optimization (RO) framework. In the RISO modeling formulation, penalties are exercised with the recourse against any infeasibility, and robustness measures are introduced to examine the variability of the second stage costs that are above that the expected levels. The RISO is generally suitable for risk-aversive planners under high-variability conditions. The RISO method is applied to a case of planning regional-scale electric power systems under consideration of CO2 trading scheme. A number of solutions under different robustness levels have been generated. They are helpful for supporting (a) adjustment or justification of allocation patterns of regional energy resources and services, (b) formulation of local policies regarding energy consumption, economic development, and energy structure, (c) analysis of the effect of CO2 trading scheme, and (d) in-depth analysis of tradeoffs between system cost and CO2-mitigation levels under total emission permissions. The modeling results from the RISO can help generate desired decision alternatives that will be able to not only enhance energy-supply safety with a low system-failure risk level but also mitigate total CO2-emissions under an effective trading scheme.