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

被引:5
|
作者
Ren, Hongtao [1 ]
Zhou, Wenji [2 ]
Wang, Hangzhou [3 ]
Zhang, Bo [4 ]
Ma, Tieju [1 ,5 ]
机构
[1] East China Univ Sci & Technol, Sch Business, Meilong Rd 130, Shanghai 200237, Peoples R China
[2] Renmin Univ China, Sch Appl Econ, Beijing 100872, Peoples R China
[3] China Natl Petr Corp, China Petr Planning & Engn Inst CPPEI, Beijing 100083, Peoples R China
[4] SINOPEC Beihai Refining & Chem Co Ltd, South 4 Rd, Beihai 536016, Guangxi, Peoples R China
[5] Int Inst Appl Syst Anal, Schlosspl 1, A-2361 Laxenburg, Austria
基金
中国国家自然科学基金;
关键词
Energy system modelling; Stochastic programming; Oil market; k-means clustering; Energy price volatility; NATURAL-GAS; CRUDE-OIL; SUPPLY CHAIN; UNCERTAINTY; COAL; MANAGEMENT; DEMAND; FUEL;
D O I
10.1007/s10479-021-04229-3
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
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.
引用
收藏
页码:555 / 579
页数:25
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