Stochastic optimization for electric power generation expansion planning with discrete climate change scenarios

This research is dedicated to the study of electric power system generation expansion planning considering uncertainty of climate change. Policy makers are increasingly concerned about the effects of climate change and its impact on human systems when making decisions. Electric power generation expansion planning (GEP) problems that determine the optimal expansion capacity and technology under particular technical constraints, given cost and policy assumptions, are undoubtedly among those decisions. The best approach to comprehensively model climate change uncertainties, and to optimize the generation planning under uncertainty needs to be rigorously studied. In this research, a preliminary GEP model is proposed with available input data from various sources. Discrete scenarios of possible climate change outcomes are defined and optimization models are formulated to specifically model uncertainty. Relationships between climate change and GEP parameters are defined for each scenario to consider their effects. The preliminary GEP model is then solved under each scenario to identify the climate change impact on generation expansion planning decisions. Two related optimization models are then presented and solved to find the optimal results under uncertainty: Model 1 is expected total cost minimization, and Model 2 is maximum regret minimization. Both models find compromise solutions that are suitable for all scenarios, which avoid the possible risk associated with a poor decision that is only optimal for one particular scenario, or only for an average climate change forecast.