A novel multi-stage fuzzy stochastic programming for electricity system structure optimization and planning with energy-water nexus - A case study of Tianjin, China. (1st January 2020) - Ji, Ling
Abstract: In this study, a novel multi-stage fuzzy stochastic programming (MSFSP) model was developed for regional energy system structure optimization and planning with energy-water nexus under multiple uncertainties. By cooperating the multistage stochastic programming and fuzzy theory, the developed model can deal with energy system planning problems under a mixture of probabilistic and possibilistic uncertainties. Fuzzy random scenarios are designed to express the uncertain future energy demand levels and vague decision maker's risk attitude. A MSFSP –based energy-water nexus system management model was applied to a practical energy system planning problem in an energy-intensive and water-stressed area, Tianjin, China. Results of optimal capacity expansion, power generation, and imported electricity strategies were obtained; meanwhile the water resource availability effects and decision makers' risk attitude were analyzed. It was found that water resource availability would be a significant factor in promoting local power structure and electricity generation in the future. More serious water resource deficiency would lead to reduce local coal-fired power capacity investment, while stimulate renewable energy development and increase imported electricity requirement. All above can facilitate decision supports for regional energy system planning from a comprehensive energy-water nexus perspective with more sustainable and risk-aversion manners. Highlights: Multi-stage fuzzy stochastic programming model for energy-water nexus management. Uncertainties were characterized by both possibility and probability distributions. The results provide deeper insight into the interrelationships between energy and water. Supporting for energy system planning under limited water sources in a sustainable way.