A scenario-based interval-stochastic basic-possibilistic programming method for planning sustainable energy system under uncertainty: A case study of Beijing, China

In this study, an interval-stochastic basic-possibilistic programming (ISBPP) method is developed for planning sustainable energy system (SES). ISBPP cannot only reflect multiple uncertainties expressed as interval-possibilistic variables and interval-stochastic-possibilistic parameters, but also facilitate analyzing tradeoff between economic objective and pollutant mitigation. The ISBPP method is then applied to planning the SES of Beijing, where different power sources for electric vehicles (EVs) are analyzed. Solutions in association with different demand levels, power sources and confidence levels have been generated. Results reveal that uncertainties existed in the system components (e.g., fuel/electricity price, capacity-expansion option, electricity demand, and vehicle ownership) have significant effects on the outputs of decision variables and system cost. Results disclose that ISBPP can provide effective supports for decision makers to achieve desired SES planning schemes with the introduction of EVs. Results also imply that reforming fossil-based energy system to a sustainable one needs both financial incentives and governmental policies on energetically developing renewable energies. Findings can help decision makers achieve the desired sustainable scheme through in-depth analysis of tradeoffs among system cost and environmental pollution control as well as EVs stimulation degree.