文献类型：期刊文献

英文题名：An energy system optimization model accounting for the interrelations of multiple stochastic energy prices

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

年份：2022

卷号：316

期号：1

起止页码：555

外文期刊名：ANNALS OF OPERATIONS RESEARCH

收录：;WOS:【SCI-EXPANDED(收录号:WOS:000691176200002)】；

基金：This research was supported by the Major Innovation & Planning Interdisciplinary Platform for the"Double-First Class" Initiative, Renmin University of China, the National Natural Science Foundation of China (71961137012, 71874055), and the International Cooperation Program of PetroChina (2018D-5009-06).

语种：英文

外文关键词：Energy system modelling; Stochastic programming; Oil market; k-means clustering; Energy price volatility

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.