A Multiobjective Evolutionary Programming Algorithm and Its Applications to Power Generation Expansion Planning

The generation expansion planning (GEP) problem is defined as the problem of determining WHAT, WHEN, and WHERE new generation units should be installed over a planning horizon to satisfy the expected energy demand. This paper presents a framework to determine the number of new generating units (e.g....

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Published in:IEEE transactions on systems, man and cybernetics. Part A, Systems and humans Vol. 39; no. 5; pp. 1086 - 1096
Main Authors: Meza, J.L.C., Yildirim, M.B., Masud, A.S.M.
Format: Journal Article
Language:English
Published: IEEE 01.09.2009
Subjects:
Algorithms
Analytical hierarchy process (AHP)
Evolutionary algorithms
evolutionary programming
Fuels
generation expansion planning (GEP)
Genetic programming
Geothermal power generation
Hydraulic turbines
Mathematical analysis
Mathematical models
multicriteria optimization
Nuclear power generation
operations research
optimization methods
Power generation
Power generation planning
Power system modeling
Sensitivity analysis
transmission expansion planning
Wind energy generation
Wind power generation
Wind turbines
ISSN:1083-4427, 1558-2426
Online Access:Get full text
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Summary:The generation expansion planning (GEP) problem is defined as the problem of determining WHAT, WHEN, and WHERE new generation units should be installed over a planning horizon to satisfy the expected energy demand. This paper presents a framework to determine the number of new generating units (e.g., conventional steam units, coal units, combined cycle modules, nuclear plants, gas turbines, wind farms, and geothermal and hydro units), power generation capacity for those units, number of new circuits on the network, the voltage phase angle at each node, and the amount of required imported fuel for a single-period generation expansion plan. The resulting mathematical program is a mixed-integer bilinear multiobjective GEP model. The proposed framework includes a multiobjective evolutionary programming algorithm to obtain an approximation of the Pareto front for the multiobjective optimization problem and analytical hierarchy process to select the best alternative. A Mexican power system case study is utilized to illustrate the proposed framework. Results show coherent decisions given the objectives and scenarios considered. Some sensitivity analysis is presented when considering different fuel price scenarios.
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ISSN:1083-4427
1558-2426
DOI:10.1109/TSMCA.2009.2025868