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A novel multi-stage fuzzy stochastic programming for electricity system structure optimization and planning with energy-water nexus - A case study of Tianjin, China

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  • Ji, Ling
  • Zhang, Beibei
  • Huang, Guohe
  • Wang, Peng

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.

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  • Ji, Ling & Zhang, Beibei & Huang, Guohe & Wang, Peng, 2020. "A novel multi-stage fuzzy stochastic programming for electricity system structure optimization and planning with energy-water nexus - A case study of Tianjin, China," Energy, Elsevier, vol. 190(C).
    • Handle: RePEc:eee:energy:v:190:y:2020:i:c:s0360544219321139
    DOI: 10.1016/j.energy.2019.116418
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