Multi-objective optimization of renewable distributed generation

What is it about?

A multi-objective genetic algorithm (MOGA) constrained methodology is proposed for the optimal allocation and sizing of renewable energy based DGs. The objectives are to minimize power losses, to minimize investment cost and to improve voltage stability while satisfying system's constraints such as power balance, voltage level, maximum power flow and maximum DG size. The MOGA is applied for finding a representative set of the Pareto optimal solutions for the three objective functions. So, a fuzzy clustering method was combined with the proposed algorithm to select the most compromising solution from the Pareto front. Finally, this method was tested for different three typical radial systems (33-bus, 69-bus and 119-bus) in order to prove its effectiveness.

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Photo by American Public Power Association on Unsplash

Photo by American Public Power Association on Unsplash

Why is it important?

Unlike the other comparable solution methodologies, the applied methodology focuses on the minimization of investment cost also as an objective. Furthermore, the applied MOGA is performed for reactive power compensation at a predetermined power factor. Thus, the obtained results with MOGA are quite promising in terms of objective achievement without violating any system operating constraints. A new analysis was done by applying the proposed MOGA to large scale power system which is 119-bus system. As a result, this methodology proves its effectiveness and robustness for application in large systems.

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