Determining optimal rated power of a photovoltaic station for mine dewatering
DOI:
https://doi.org/10.15587/1729-4061.2022.267034Keywords:
pumping unit, dewatering system, photovoltaic module, rated capacity, optimization criterionAbstract
The object of the study is the main dewatering system of the mine. Climate change has stimulated the refusal to use coal in many countries. In conditions of massive closure of mines, there is a need to pump out mine waters to avoid flooding. A significant water influx determines the high cost of electricity consumed by pumps. It is proposed to increase the efficiency of the mine dewatering system due to the introduction of a smart power supply grid with photovoltaic generation. The relative value of the annual balance of payment for energy consumption is chosen as an optimization parameter. The rated capacity of the photovoltaic station is optimized according to the criterion of approaching, with accuracy up to the permissible mismatch, the absolute value of the optimization parameter to zero. The relationship between the optimization parameter and the rated capacity of the photovoltaic station is represented by a parabolic regression. Regression parameters are estimated in the case study for a specific mine based on the results of a single-factor simulation experiment conducted using a computer model of a smart power grid. The randomness of natural, technical and economic factors is taken into account. Based on the prediction intervals for the regression, the optimal rated capacity of the photovoltaic station for the selected mine is estimated at 3.164 MW with a pump capacity of 1.732 MW. It was found that the annual energy savings for the case study conditions reach 3,745 MWh. Equipping the power supply grid of the main dewatering system with a photoelectric station of optimal configuration will reduce the cost of consumed electricity to several percent. This will make it possible to avoid financial costs for maintaining the balance of underground water and reduce the flooding probability of coal regions being transformed
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