Stochastic modeling of the energy supply system with uncertain fuel price - A case of emerging technologies for distributed power generation

Mirkhani, S ; Sharif University of Technology | 2012

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  1. Type of Document: Article
  2. DOI: 10.1016/j.apenergy.2011.12.099
  3. Publisher: 2012
  4. Abstract:
  5. A deterministic energy supply model with bottom-up structure has limited capability in handling the uncertainties. To enhance the applicability of such a model in an uncertain environment two main issues have been investigated in the present paper. First, a binomial lattice is generated based on the stochastic nature of the source of uncertainty. Second, an energy system model (ESM) has been reformulated as a multistage stochastic problem. The result of the application of the modified energy model encompasses all uncertain outcomes together and enables optimal timing of capacity expansion. The performance of the model has been demonstrated with the help of a case study. The case study has been formulated on the assumption that a gas fired engine competes with renewable energy technologies in an uncertain environment where the price of natural gas is volatile. The result of stochastic model has then been compared with those of a deterministic model by studying the expected value of perfect information (EVPI) and the value of stochastic solution (VSS). Finally the results of the sensitivity analysis have been discussed where the characteristics of uncertainty of the price of fuel are varied
  6. Keywords:
  7. Distributed generation ; Real Option ; Binomial lattice ; Capacity expansion ; Deterministic models ; Emerging technologies ; Energy model ; Energy supplies ; Energy supply system ; Energy system model ; Expected values ; Fuel prices ; Gas-fired ; Geometric Brownian motion ; Optimal timing ; Real Options ; Renewable energy technologies ; Stochastic modeling ; Stochastic nature ; Stochastic problems ; Stochastic solution ; Uncertain environments ; Brownian movement ; Distributed power generation ; Stochastic programming ; Stochastic systems ; Stochastic models ; Natural gas ; Numerical model ; Performance assessment ; Power generation ; Price determination ; Renewable resource ; Sensitivity analysis ; Stochasticity ; Technological development ; Uncertainty analysis
  8. Source: Applied Energy ; Volume 93 , 2012 , Pages 668-674 ; 03062619 (ISSN)
  9. URL: http://www.sciencedirect.com/science/article/pii/S0306261912000049