Electric power systems planning in association with air pollution control and uncertainty analysis

In this study, a multistage stochastic full-infinite integer programming (MSFIP) method is developed for planning electric-power systems associated with multiple uncertainties presented in terms of crisp intervals, probability distributions, functional intervals and integer variables. Compared with the existing parametric programming method, MSFIP can not only deal with the complex tradeoff between systems cost minimization and pollution-emission mitigation, but also reflect the dynamics through generation of a set of representative scenarios within a multistage context. A case study for regional-scale electric power systems is provided for demonstrating the applicability of the MSFIP, where energy resources, economic concerns, and environmental requirements are integrated into a systematic optimization framework. In the MSFIP model, electricity shortages are exercised with recourse against any infeasibility, which permits in-depth analyses of various policy scenarios that are associated with different levels of economic consequences when the promised electric supply targets are violated. It is indicated that MSFIP model is able to help for lowering the risk of system failure due to potential violation when determining optimal electricity remediation strategies. The modeling results can help to generate a range of alternatives under various system conditions, and thus help decision makers to identify desired policies, including electricity supply, facility capacity expansion and air-pollutant control under multiple uncertainties.