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A Method for Assessing the Impact of Changes in Demand for Coal on the Structure of Coal Grades Produced by Mines

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  • Dariusz Fuksa

    (Faculty of Civil Engineering and Resource Management, AGH University of Science and Technology, Mickiewicza 30 Av., 30-059 Kraków, Poland)

Abstract

Due to the withdrawal of coal from power generation in the EU, mining companies in Poland are forced to adapt their production to the decreasing demand. Forecasting the volume of demand plays an important role in planning the volume of the mine’s output. The demand for coal is constantly changing, with a downward trend. This article presents a method that allows to assess the impact of the variable demand on mine profits and on the volumes of sales of individual coal grades. The proposed method is based on the Monte Carlo simulation and on a solution consisting of the optimization of the production and sales of coal by the mining company (the SIMPLEX algorithm). By using the Monte Carlo simulation to forecast the demand, unlike other commonly used methods, a sufficiently large set of real situations that may occur in the future can be obtained. The results allow us to conclude the extent of desirable adjustment of the structure of the mine’s production to the requirements of its consumers, as well as to predict in which direction these changes will proceed and with what probability. The usefulness of the developed method has been verified on the example of an existing hard coal mine.

Suggested Citation

  • Dariusz Fuksa, 2021. "A Method for Assessing the Impact of Changes in Demand for Coal on the Structure of Coal Grades Produced by Mines," Energies, MDPI, vol. 14(21), pages 1-34, November.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:21:p:7111-:d:669671
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    References listed on IDEAS

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    1. Smith, Michael, 2000. "Modeling and Short-term Forecasting of New South Wales Electricity System Load," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(4), pages 465-478, October.
    2. Chong, ChinHao & Ma, Linwei & Li, Zheng & Ni, Weidou & Song, Shizhong, 2015. "Logarithmic mean Divisia index (LMDI) decomposition of coal consumption in China based on the energy allocation diagram of coal flows," Energy, Elsevier, vol. 85(C), pages 366-378.
    3. Michieka, Nyakundi M. & Fletcher, Jerald J., 2012. "An investigation of the role of China's urban population on coal consumption," Energy Policy, Elsevier, vol. 48(C), pages 668-676.
    4. Joanna Nowicka-Zagrajek & Rafal Weron, 2002. "Modeling electricity loads in California: ARMA models with hyperbolic noise," HSC Research Reports HSC/02/02, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
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    Keywords

    algorithm simplex; Monte Carlo simulation;

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