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Comparison of Mixed-Integer Programming and Genetic Algorithm Methods for Distributed Generation Planning
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DOI：10.1109/TPWRS.2013.2287880.png)
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This paper applies recently developed mixed-integer programming (MIP) tools to the problem of optimal siting and sizing of distributed generators in a distribution network. We investigate the merits of three MIP approaches for finding good installation plans: a full AC power flow approach, a linear DC power flow approximation, and a nonlinear DC power flow approximation with quadratic loss terms, each augmented with integer generator placement variables. A genetic algorithm-based approach serves as a baseline for the comparison. A simple knapsack problem method involving generator selection is presented for determining lower bounds on the optimal design objective. Solution methods are outlined, and computational results show that the MIP methods, while lacking the speed of the genetic algorithm, can find improved solutions within conservative time requirements and provide useful information on optimality.
Keyword:
Distributed power generation
genetic algorithms
integer linear programming
nonlinear programming
quadratic programming
期刊
IF:
7.2
论文数:
1.1W
被引数:
5.0W
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