205
Views
50
CrossRef citations to date
0
Altmetric
Original Articles

IPEM: An Interval-parameter Energy Systems Planning Model

&
Pages 1382-1399 | Published online: 18 Jun 2008
 

Abstract

Energy systems planning models are specifically developed for effective planning of energy activities in a regional, national, or global context. However, the planning process is fraught with uncertainties that may affect the effectiveness of the planning. In this study, an interval-parameter linear programming approach is introduced to develop an interval-parameter energy systems model (IPEM) for supporting effective regional energy systems planning under uncertainty. The developed methodology is then applied to a hypothetical regional energy system. The results strongly suggest that this innovative approach can effectively handle the uncertain information expressed as intervals in the energy planning process and provide more satisfactory solutions for the optimization problem of energy allocation and capacity expansion within a regional jurisdiction. Compared with other energy systems models, this model generates two schemes corresponding to the upper and the lower bounds of system objective, which represent two extreme decisions regarding environmental-economic trade-off. The interval solutions allow for detailed interpretation of the trade-off between environmental pollution risks and economic objectives.

Acknowledgments

This research was supported by the Major State Basic Research Development Program of MOST (2005CB724200 and 2006CB403307) and the Natural Science and Engineering Research Council of Canada.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.