Planning municipal-scale mixed energy system for stimulating renewable energy under multiple uncertainties - The City of Qingdao in Shandong Province, China

Rapidly increased coal consumption has caused dramatically pollutants and carbon dioxide (CO2) emissions, such that utilizing renewable energy to substitute high pollutant and carbon fuels is crucial to fulfill sustainable development. In this study, an interval possibilistic-stochastic programming (IPSP) method that is capable of dealing with multiple uncertainties existed in the real-world mixed energy system (MES) is developed. Then, the IPSP method is applied to planning MES in the City of Qingdao (China) that aims to encourage developing renewable energies based on subsidy policy. Solutions under varied subsidies for stimulating renewable energies in association with different probabilities and α levels have been generated, which can help determine the optimized electricity generation and supply that could hedge appropriately against system violation risk. Compared to the results without subsidy, renewable energies with subsidy policy can increase by [2.4, 3.2] % and the share of renewable energies would raise to 17.5% at the end of planning horizon. The findings can provide useful information for the other municipal-scale MES planning issues to facilitate policy enactment of renewable energy, improvement of energy supply security, as well as accomplishment of planning environmental and sustainable MES. •An interval possibilistic-stochastic programming (IPSP) method is developed.•IPSP can handle multiple uncertainties under different probabilities and α levels.•The IPSP method is applied to planning mixed energy system (MES) of Qingdao, China.•Solutions under various subsidies for stimulating renewable energy are analyzed.•Results can facilitate policy enactment of renewable energy and improve MES security.