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An improved genetic algorithm for generation expansion planning
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DOI：10.1109/59.871713.png)
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This paper presents a development of an improved genetic algorithm (TGA) and its application to a least-cost generation expansion planning (GEP) problem. Least-cost GEP problem is concerned with a highly constrained nonlinear dynamic optimization problem that can only be fully solved by complete enumeration, a process which is computationally impossible in a real-world GEP problem. in this paper, an improved genetic algorithm incorporating a stochastic crossover technique and an artificial initial population scheme is developed to provide a faster search mechanism. The main advantage of the IGA approach is that the curse of dimensionality and a local optimal trap inherent in mathematical programming methods can be simultaneously overcome. The IGA approach is applied to two test systems, one with 15 existing power plants, 5 types of candidate plants and a 14-year planning period, and the other, a practical Long-term system with a 24-year planning period.
Keyword:
generation expansion planning
genetic algorithm
global optimization
improved genetic algorithm
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IF:
7.2
论文数:
1.1W
被引数:
5.0W
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