An inexact optimization model for regional electric system steady operation management considering integrated renewable resources. (15th September 2017) - Zhen, J.L.; Huang, G.H.; Li, W.; Liu, Z.P.; Wu, C.B.
Abstract: In this study, an inexact two-stage stochastic fuzzy programming (ITSFP) is developed for regional power generation planning with considering the intermittency and fuzziness of renewable energy power output. ITSFP incorporates interval-parameter programming (IPP), two-stage stochastic programming (TSP), and fuzzy credibility constrained programming (FCCP) within a general optimization framework which can tackle uncertainties expressed as intervals, probability distributions, and fuzzy sets. The developed method is applied to a regional electric power system over a one-day optimization horizon coupled with air pollution control. The power generation schemes, imported electricity, and system cost under various environmental goals and risk preferences are analyzed. The obtained results indicate that the model can provide a linkage between predefined electric power generation schedule and the relevant economic implications, as well as more reasonable decision alternatives for decision makers by loosening system constraints at specified confidence level. Besides, the fuzziness of forecast error corresponding to the variability of renewable energy resources could be effectively reflected. Moreover, the results are useful for addressing the trade-off between system economy and system risk. Highlights: An inexact stochastic-fuzzy programming is proposed for generation scheduling. Uncertainties are expressed as intervals, probability distributions and fuzzy sets. Power generation schemes, imported electricity, and system cost are analyzed. Alternative solutions associated with different confidence levels are obtained. Tradeoffs between system economic and reliability risk could be evaluated.