A flexible-possibilistic stochastic programming method for planning municipal-scale energy system through introducing renewable energies and electric vehicles

Excessive stress on fossil resources has deteriorated energy crisis and environmental problem, such that introducing renewable energies and electric vehicles (EVs) has become a main concern for government. In this study, a flexible-possibilistic stochastic programming (FPSP) method is developed for planning municipal-scale energy system (MES) with cost minimization and emission mitigation. FPSP cannot only deal with multiple uncertainties employed to the soft constraints and objective function, but also analyze the individual and interactive effects of uncertain parameters on system cost. The FPSP method is then applied to planning MES of Beijing under considering the impacts of renewable energies and EVs. Solutions in association with different constraint-violation levels, satisfaction degrees and confidence levels have been obtained. Results disclose that introducing EVs to the study MES can effectively mitigate pollutant emissions, and the emissions of sulphur dioxide (SO2), nitrogen oxide (NO) and inhalable particles (PM10) can be reduced 7.9%, 10.8% and 9.1%, respectively. Results also imply that the city's MES can be adjusted towards a cleaner pattern through developing renewable energies and EVs. Findings can provide support for planning energy system through introducing EVs to high-traffic city and offer scientific information to decision makers for mitigating pollutant emissions under multiple uncertainties. (C) 2018 Elsevier Ltd. All rights reserved.