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

Hongtao Ren, Wenji Zhou (), Hangzhou Wang, Bo Zhang and Tieju Ma
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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

Annals of Operations Research, 2022, vol. 316, issue 1, No 21, 555-579

Abstract: 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.

Keywords: Energy system modelling; Stochastic programming; Oil market; k-means clustering; Energy price volatility (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s10479-021-04229-3

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