A dynamic model to optimize municipal electric power systems by considering carbon emission trading under uncertainty

In this study, a FFSP (full-infinite fuzzy stochastic programming) method is developed for planning MEPS (municipal electric power systems) associated with GHG (greenhouse gas) control under uncertainty. FFSP can deal with multiple uncertainties presented in terms of fuzzy sets, functional intervals, and random variables. FFSP is also applied to a case study of Beijing for managing MEPS, and reducing the GHG emission through introducing the EU ETS (European Union greenhouse gas emission trading scheme). The results indicate that reasonable solutions have been generated, which can be used for generating schemes of energy resources, electricity production/allocation, and capacity expansion under various economic costs and GHG reduction requirements. The case study demonstrates that FFSP can increase the abilities of reflecting complexities for dynamics of capacity expansion and interaction of multiple uncertainties in MEPS. The results allow in-depth analyses of trade-offs between GHG mitigation and economic objective as well as those between system cost and decision makers' satisfaction degree. Besides, this study can also provide an example to help China construct domestic carbon trading market at municipal scale for addressing the challenges of global climate change. - Highlights: • A dynamic optimization (FFSP) method is developed for tackling uncertainties. • FFSP is applied to planning MEPS (municipal electric power systems) of Beijing. • CET (Carbon emission trading) is introduced into MEPS for mitigating CO₂ emissions. • Trade-offs occur between system cost and satisfaction degree under uncertainties. • Results can provide an example to construct domestic CET market in China