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An energy system optimization model accounting for the interrelations of multiple stochastic energy prices

Author

Listed:
  • Hongtao Ren

    (East China University of Science and Technology)

  • Wenji Zhou

    (Renmin University of China)

  • Hangzhou Wang

    (China National Petroleum Corporation)

  • Bo Zhang

    (SINOPEC Beihai Refining and Chemical Co., Ltd.)

  • Tieju Ma

    (East China University of Science and Technology
    International Institute for Applied Systems Analysis)

Abstract

The variation of and the interrelation between different energy markets significantly affect the competitiveness of various energy technologies, therefore complicate the decision-making problem for a complex energy system consisting of multiple competing technologies, especially in a long-term time frame. The interrelations between these markets have not been accounted for in the existing energy system modelling efforts, leading to a distortion of understanding of the market impact on the technological choices and operations in the real world. This study investigates the strategic and operational decision-making problem for such an energy system characterized by three competing technologies from crude oil, natural gas, and coal. A stochastic programming model is constructed by incorporating multiple volatile energy prices interrelated with each other. Oil price is modelled by the mean-reverting Ornstein–Uhlenbeck process and serves as the exogenous variable in the ARIMAX models for natural gas and downstream plastic prices. The K-means clustering method is employed to extract a handful of distinctive patterns from a large number of simulated price projections to enhance the computing efficiency without losing retaining critical information and insights from the price co-movement. The model results suggest that the high volatility of the energy market weakens the possibility of selecting the corresponding technology. The oil-based route, for example, gradually loses its market share to the coal approach, attributed to a higher volatile oil market. The proposed method is applicable to other problems of the same kind with high-dimensional stochastic variables.

Suggested Citation

  • Hongtao Ren & Wenji Zhou & Hangzhou Wang & Bo Zhang & Tieju Ma, 2022. "An energy system optimization model accounting for the interrelations of multiple stochastic energy prices," Annals of Operations Research, Springer, vol. 316(1), pages 555-579, September.
    • Handle: RePEc:spr:annopr:v:316:y:2022:i:1:d:10.1007_s10479-021-04229-3
    DOI: 10.1007/s10479-021-04229-3
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