Abstract

Although geothermal resources are practically independent of climate factors, those factors significantly condition the potential use of the Earth’s natural heat resources. Unlike all the other factors limiting or facilitating the use of geothermal heat (like receivers’ temperature expectation, financial issues or local regulations), climate factors remain immovable. Thus, climate remains the main factor influencing the effective use of geothermal resources. Volumes of sold energy, typical capacity factors and rapid changes in heat demand may all influence the financial and technological performance of an investment. In the current paper, climate factors are translated into heat demand based on historical data (meteorological and district heating logs) by means of a dedicated artificial neural network, and analysed in terms of possible constraints and facilitators that might affect the effective use of geothermal energy. The results of ANN simulation indicate that average and typical operation is expected without any turbulences, yet about 10% of operating hours may require additional technical measures, like peak source support, smart management and buffers in order to limit pumping ramp rate. With appropriate dimensioning and exploitation, capacity factors as high as 60% are available, proving the potential for financially and environmentally effective use of geothermal resources.

Highlights

  • Geothermal energy is suited to providing heat as a base source, both in rural and urban areas, using local and sustainable heat

  • Bulk observations around the Lublin trough are similar, which is to be expected in light of the distances involved being less than 300 km

  • The most general one is that climatological conditions facilitate the use of geothermal energy in the Lublin trough

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Summary

Introduction

Geothermal energy is suited to providing heat as a base source, both in rural and urban areas, using local and sustainable heat. Development-driven conditions, like number and size of potential heat receivers, and climate-driven conditions, like duration of heating season and amount of energy sold per dwelling, are of the highest importance when decisions are made (Lund and Lienau 2009). Detailed computations, like those proposed by Lund and Lienau, that include climatological data are usually performed for specific locations.

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