The impacts of technological learning on the optimum technology mix: simulations for the Indian power sector

Jyotirmay Mathur, Narendra Kumar Bansal and Hermann-Joseh Wagner

International Journal of Energy Technology and Policy, 2004, vol. 2, issue 3, 240-249

Abstract: For the investigation of the optimum technology mix of any country, which is clearly an issue of dynamic nature, technological learning and economies of scale play a significant role. Hence, in this paper's long term planning exercise for the Indian power sector (2000-2025), our simulation analysis specifically includes the impacts of technological learning on the optimal inter-temporal choice of power generation technologies. Based on dynamic linear programming methods and MARKAL, a software tool for power generation capacity planning, the most significant result of our analysis is that among various renewable energy technologies, technological learning will favour wind and small hydropower generation, while pressurised fluidised bed combustion-based coal power plants appear to be the favourite conventional fossil fuel-based technology in India.

Keywords: energy planning; technological learning; India; technology mix; economies of scale; simulation; power generation; capacity planning; renewable energy; wind power; wind energy; small hydropower; fossil fuels; coal; fluidised bed; combustion; power plants. (search for similar items in EconPapers)
Date: 2004
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.inderscience.com/link.php?id=5156 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ids:ijetpo:v:2:y:2004:i:3:p:240-249

Access Statistics for this article

More articles in International Journal of Energy Technology and Policy from Inderscience Enterprises Ltd
Bibliographic data for series maintained by Sarah Parker ().