Power generation expansion planning in an autonomous island system using multi-objective programming: the case of Milos Island

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. CO 2 emission reduction maximization) subject to a num...

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Bibliographic Details
Published in:Operational research Vol. 10; no. 1; pp. 109 - 132
Main Authors: Kourempele, Maria, Mavrotas, G., Geronikolou, L., Rozakis, S.
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer-Verlag 01.05.2010
Springer Nature B.V
Subjects:
Business and Management
Carbon dioxide
Computational Intelligence
Decision making
Demand
Electric power
Electricity distribution
Energy consumption
Energy economics
Fuzzy logic
Fuzzy sets
Islands
Linear programming
Load
Management Science
Marketing
Mathematical models
Mathematical programming
Maximization
Methods
Operations Research
Operations Research/Decision Theory
Original Paper
Pollutants
Power generation
Power supply
Studies
Sustainability
Sustainable development
Turbines
Variables
Greece
Fuzzy demand
Multi-objective mixed integer linear programming
Reference point
Augmented ε-constraint method
Energy regional planning
ISSN:1109-2858, 1866-1505
Online Access:Get full text
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Summary: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. CO 2 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 CO 2 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.
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ISSN:1109-2858
1866-1505
DOI:10.1007/s12351-009-0063-5