Tools
Tools
Ren, H., Zhou, W., Wang, H., Zhang, B., & Ma, T. (2021). An energy system optimization model accounting for the interrelations of multiple stochastic energy prices. Annals of Operations Research 10.1007/s10479-021-04229-3.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Energy price volatility; Energy system modelling; Oil market; Stochastic programming; k-means clustering |
| Research Programs: | Energy, Climate, and Environment (ECE) Energy, Climate, and Environment (ECE) > Sustainable Service Systems (S3) Energy, Climate, and Environment (ECE) > Transformative Institutional and Social Solutions (TISS) |
| Depositing User: | Luke Kirwan |
| Date Deposited: | 13 Sep 2021 11:07 |
| Last Modified: | 13 Sep 2021 11:07 |
| URI: | https://pure.iiasa.ac.at/17422 |
Actions (login required)
 |
View Item |