Optimal capacitor placement in distorted distribution networks with different load models using Penalty Free Genetic Algorithm

• Fitness function equal to the objective function.
• No penalty parameter nor weight coefficients tuning nor handling/exploit of constraints structure.
• No sensitivity analysis for selection/reduction of buses for capacitor placement.
• Use of constant power, current and impedance load model shows impact on savings.
• Sparse matrix formulation and oriented branch numbering for increased calculation speed.

Genetic Algorithm with special constraint handling procedure is proposed for the discrete optimization problem of capacitor placement and sizing in distribution system for cost reduction and power quality improvement. We use gene encoding that enables simple integer representation of possible different number of capacitors of various standard sizes to be placed on a bus. A pair-wise comparison in tournament selection operator is used so that it does not require any penalty parameter tuning, thus avoiding the most difficult aspect of the selection of appropriate penalty parameters.Proposed Penalty Free Genetic Algorithm (PFGA) is tested on 18-bus, 69-bus and 141-bus systems and the obtained results are better than the results from other methods. Simulations with different load models are also performed. It is shown that load models where active and reactive loads are voltage dependent, such as residential, commercial and industrial, constant Z and constant I model lead to completely different solutions. Therefore, careful load modeling should be put in place in order to obtain more realistic picture of the total savings.