Optimal Planning of Distributed Generation via Nonlinear Optimization and Genetic Algorithms

Pisică, Ioana; Postolache, Petru; Edvall, Marcus M.

doi:10.1007/978-3-642-02493-1_20

Cited by 2 publications

(1 citation statement)

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“…Downloaded by [University of Tasmania] at 10:00 01 October 2015 New candidate solutions are created from the previous set by genetic operators (crossover and mutation). In any generation, the solution with the highest fitness represents the optimal point in the search space (Pisicȃ, Postolache, and Edvall 2010). In this work, the ALGA ("Global Optimization" 2004-2015) is used to solve the nonlinear-constrained problem.…”

Section: Ga Optimisation Using Matlabmentioning

confidence: 99%

Genetic single objective optimisation for sizing and allocation of renewable DG systems

Hassan

Fahmy

Nafeh

et al. 2015

International Journal of Sustainable Energy

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The optimal design of renewable-based distributed generations (DGs) is a challenging issue in order to maximise their benefits and to overcome power quality problems. Therefore, this paper proposes a methodology for optimal allocation and sizing of renewable DG units to minimise total power losses over radial distribution systems. The planning problem is formulated as a single objective nonlinear mixed integer-constrained optimisation problem and is solved by using the augmented Lagrangian genetic algorithm (ALGA) by combining the objective function and the nonlinear constraints. In that case, the ALGA solves a sequence of sub-problems where the objective function penalises the constraints violation in order to obtain the best solution. The proposed technique is applied to IEEE radial test systems including 33-bus, 69-bus and 119-bus and is implemented with different scenarios including all possible combinations and various types of renewable DG units to prove the effectiveness of the proposed methodology.

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Section: Ga Optimisation Using Matlabmentioning

confidence: 99%