Importance of fuel in the valuation of lignite-based energy projects with risk assessment from geology to energy market

• Published in: Fuel (Guildford) Vol. 209; pp. 694 - 701
• Main Authors: Dudek, Michał, Krysa, Zbigniew, Jurdziak, Leszek, Kawalec, Witold
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.12.2017; Elsevier BV
• Subjects: Carbon dioxide; Computer simulation; Electricity pricing; Energy; Energy deposit; Error analysis; Fuels; Geology; Geostatistics; Kriging; Land acquisition; Land use; Lerchs-Grossmann optimisation; Lignite; Mass-energy balance; Mining; Mining economics; Power efficiency; Power station efficiency; Risk assessment; Sensitivity analysis; Surface cost; Sustainable use; Three dimensional models; Lerchs-Grossmann optimisation; Lignite; Energy deposit; Mining economics; Mass-energy balance; Surface cost; Power station efficiency
• ISSN: 0016-2361; 1873-7153
• DOI: 10.1016/j.fuel.2017.08.041

•A complex optimisation model of lignite energy generation project is proposed.•Lignite energy projects are sensitive to energy price and carbon tax change.•Some non-CCS projects may not be economically viable to compensate high carbon tax.•Lack of surface costs analysis may lead to misinformed economic viability estimation.•Joint optimisation with surface cost map aids investment decision-making process. This research aims to discuss complex economics of lignite-based energy projects with respect to risk and uncertainty, optimisation, sustainable land use and the importance of lignite as fuel that may be expressed in situ as a deposit of energy. The sensitivity analyses and Monte Carlo simulations performed in this article include estimated land acquisition costs, geostatistics, 3D deposit block modelling, electricity (product) price, power station efficiency, the unit cost of lignite processing at the power station, CO2 allowance costs, mining unit cost and also geological risk considered as kriging estimation error for lignite reserves. The investigated parameters have a nonlinear influence on the final results and hence the economically viable amount of lignite in the optimum ultimate pit varies. The optimum ultimate pit area varies across scenarios from 11.2km2 (or even 9.1km2) up to 14.3km2. The performed simulations allowed each optimum ultimate pit to be calculated from a unique set of project parameters based on their distributions. For the highest surface cost scenario, there is 95% probability of obtaining undiscounted net value of €1277 million and also there is only 5% chance to obtain the net value of €5524 million.