Optimization of electric power systems with cost minimization and environmental-impact mitigation under multiple uncertainties

摘要

A multistage inexact-factorial fuzzy-probability programming (MIFF) method is developed for optimizing electric power systems with cost minimization and environmental-impact mitigation. MIFF is capable of addressing parameter uncertainties presented as intervals/fuzzy-probability distributions and their interactions in a systematic manner over a multistage context; it can also quantitatively evaluate the individual and interactive effects on system performance. The proposed MIFP method is then applied to planning electric power system for the City of Qingdao, where multiple scenarios that emission-reduction target is designed as random variable and electricity demand is specified as fuzzy-probability distribution over a long term are analyzed. Results reveal that various uncertainties in system components (e.g., fuel price, electricity-produce cost, emission-mitigation option, and electricity-demand level) have sound effects on the city's future energy systems. High mitigation and high demand correspond to decisions with considerable efforts for developing more renewable energies to reduce pollutants and carbon dioxide emitted from fossil fuels. Results also disclose that the proportion of electricity generated by coal would shrink with time to reduce the environmental negative impacts. The imported electricity would eventually drop as the local renewable energy capacity becomes capable of meeting the city's electricity demand Through developing renewable energy, the city's electric power system could finally be adjusted towards a cleaner and safer pattern. Results also show that factors of electricity demand and import electricity expenditure have significant individual and/or joint effects on the system cost. The findings can not only optimize electricity-generation and-supply patterns with a cost-effective manner, but also help decision makers identify desired strategies for enhancing the mitigation of environmental impacts under uncertainty.