| dc.contributor.author |
Kourempele, M |
en |
| dc.contributor.author |
Mavrotas, G |
en |
| dc.contributor.author |
Geronikolou, L |
en |
| dc.contributor.author |
Rozakis, S |
en |
| dc.date.accessioned |
2014-06-06T06:50:49Z |
|
| dc.date.available |
2014-06-06T06:50:49Z |
|
| dc.date.issued |
2010 |
en |
| dc.identifier.issn |
11092858 |
en |
| dc.identifier.uri |
http://dx.doi.org/10.1007/s12351-009-0063-5 |
en |
| dc.identifier.uri |
http://62.217.125.90/xmlui/handle/123456789/5174 |
|
| dc.subject |
Augmented ε-constraint method |
en |
| dc.subject |
Energy regional planning |
en |
| dc.subject |
Fuzzy demand |
en |
| dc.subject |
Multi-objective mixed integer linear programming |
en |
| dc.subject |
Reference point |
en |
| dc.title |
Power generation expansion planning in an autonomous island system using multi-objective programming: The case of Milos island |
en |
| heal.type |
journalArticle |
en |
| heal.identifier.primary |
10.1007/s12351-009-0063-5 |
en |
| heal.publicationDate |
2010 |
en |
| heal.abstract |
This paper presents the application of multiple objective linear programming for power generation expansion planning on Milos island. The model considers those economic and environmental objectives which typically conflict (cost minimization vs. CO2 emission reduction maximization) subject to a number of constraints. Due to uncertainties in future power demand, the latter is handled as a fuzzy parameter. Fuzziness is dealt with by the addition of a third objective function, the maximization of the degree of demand satisfaction. The MOLP model developed is solved in two ways. First, the use of the augmented ε-constraint method which produces the trade offs between cost and CO2 for different values of the degree of demand satisfaction, and second the reference point framework, a generation and an interactive method, respectively. In the second approach decision makers set their aspiration levels concerning the different criteria, converging after a number of iterations in a compromise solution, whereas in the augmented ε-constraint generation method the decision makers have to choose their preferred solution between all the efficient points depicted in the trade-offs. A comparative analysis of the above methods concludes the paper, highlighting advantages and shortcomings. © Springer-Verlag 2009. |
en |
| heal.journalName |
Operational Research |
en |
| dc.identifier.issue |
1 |
en |
| dc.identifier.volume |
10 |
en |
| dc.identifier.doi |
10.1007/s12351-009-0063-5 |
en |
| dc.identifier.spage |
109 |
en |
| dc.identifier.epage |
132 |
en |