Sustainable Mining Land Use for Lignite Based Energy Projects

This research aims to discuss complex lignite based energy projects economic viability and its impact on sustainable land use with respect to project risk and uncertainty, economics, optimisation (e.g. Lerchs and Grossmann) and importance of lignite as fuel that may be expressed in situ as deposit of energy. Sensitivity analysis and simulation consist of estimated variable land acquisition costs, geostatistics, 3D deposit block modelling, electricity price considered as project product price, power station efficiency and power station lignite processing unit cost, CO2 allowance costs, mining unit cost and also lignite availability treated as lignite reserves kriging estimation error. Investigated parameters have nonlinear influence on results so that economically viable amount of lignite in optimal pit varies having also nonlinear impact on land area required for mining operation.


Introduction
With high fixed costs, optimal surface costs and transition to higher efficiency power generation became important issue to sustain high profitability of lignite based energy projects in a low carbon future. Joint analyses constitute a contribution to investment decision-making [1,2,3,4] and sustainable land use so that no land is used without careful economic analysis. Lignite reserves represent a subset of resources, which could be mined economically with regard to realistic mining and economic conditions at the time of reporting. In order to identify lignite reserves at least the ultimate pit shell has to be designed [5]. Thanks to optimisation [6,7] and modelling a graphical feedback of each scenario pit extents is given enabling precise cadastral visualization and analyses of occupied land, which might be helpful in terms of mining-induced displacement and resettlement [8]. Although research was performed for lignite deposit, it can be easily adapted to coal deposit simply by changing block model and some further adjustments.

Test model development
Estimation of the minable lignite reserves should be based on the spatial model of the lignite deposit and defined pit limits. Spatial modelling is crucial for both, simple and more complex geological structures such as multi-seam deposits [9]. To create lignite deposit economic block model, quality parameters were investigated. Under intrinsic stationarity assumption kriging estimation was performed. Kriging as estimation procedure gives the best linear unbiased prediction of the values and by assigning kriging weights, kriging variance (kriging error) is minimized [

Lignite quality index adjusted to energy
Estimation in order to estimate lignite deposit value, quality index is introduced as part of lignite price formula (multiplication of quality index and base price) that was used by Polish lignite mine to estimate the price of lignite -product sold to power plant. Quality index enable to differentiate lignite through deposit.
To prepare joint optimisation of lignite mine and power plant it is important to locate investment in existing cadastral land scheme. Analysed map includes about 6 000 land parcels that were grouped into 2 main categories as for urban and rural area. Within each category, there are 5 types of land use: residential, agricultural, build-up agricultural, forests and shrubs, and other (roads, infrastructure etc.). For each of 10 land use gamma probability density function of real estate free market transactions prices were plotted. Free market transactions data that were used were derived from Real Estate Turnover [11] prepared by Polish Central Statistical Office based upon descriptive characteristics of average transaction prices of premises at County level that comprises thousands of transactions. For simulations and sensitivity analyses 4 surface cost scenarios were used which include the no surface cost scenario for comparison purposes and also 50 th , 60 th and 95 th percentile of surface cost scenarios. Importance of real estate surface cost was also investigated before [12,13].

Results and discussions
In simulations and sensitivity analyses total number of 596 optimum pits were calculated with variable project parameters as shown in table 1. These 596 optimum pits extents can be used for precise cadastral visualization and analyses of occupied land for each scenario if required.    Analyses show that project is most sensitive to energy (product) price changes what implies the most significant changes in occupied land area. In figure 9, outlines of ultimate pit extents for two surface cost scenarios are presented as calculated in sensitivity analyses. Green outline corresponds to 0 th percentile as no surface cost scenario whereas red outline corresponds to 95 th percentile of surface cost.

Conclusions
Lignite open pit mining electricity generation project has many uncertainties that can be properly identified only in combination of financial, geological, real estate, technical and optimisation assessment. With high fixed costs, optimal surface costs and transition to higher efficiency power generation became very important issue to sustain high profitability of lignite based energy projects in a low carbon future. Multi parameter analysis might be helpful not only to extractive industry executives but to determine sustainable mining land development in dense urban area. Resulting optimum pit outline can include ID of cadastral land parcels, which might be helpful for decision makers and further studies.