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The problem of optimal placement and sizing (OPS) of renewable distributed generation (RDG) is followed by numerous technical, economical, geographical, and ecological constraints. In this paper, it is investigated from two viewpoints, namely the simultaneous minimization of total energy loss of a distribution network and the maximization of profit for RDG owner. The stochastic nature of RDG such as the wind turbine and photovoltaic generation is accounted using suitable probabilistic models. To solve this problem, a hybrid metaheuristic algorithm is proposed, which is a combination of the phasor particle swarm optimization and the gravitational search algorithm. The proposed algorithm is tested on an IEEE 69-bus system for several cases in two scenarios. The results obtained by the hybrid algorithm shows that it provides high-quality solution for all cases considered and has better performances for solving the OPS problem compared to other metaheuristic population-based techniques.

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This paper presents a genetic algorithm (GA) based approach for the solution of the optimal power flow (OPF) in distribution networks with distributed generation (DG) units, including fuel cells, micro turbines, diesel generators, photovoltaic systems and wind turbines. The OPF is formulated as a nonlinear multi-objective optimization problem with equality and inequality constraints. Due to the stochastic nature of energy produced from renewable sources, i.e. wind turbines and photovoltaic systems, as well as load uncertainties, a probabilisticalgorithm is introduced in the OPF analysis. The Weibull and normal distributions are employed to model the input random variables, namely the wind speed, solar irradiance and load power. The 2m+1 point estimate method and the Gram Charlier expansion theory are used to obtain the statistical moments and the probability density functions (PDFs) of the OPF results. The proposed approach is examined and tested on a modified IEEE 34 node test feeder with integrated five different DG units. The obtained results prove the efficiency of the proposed approach to solve both deterministic and probabilistic OPF problems for different forms of the multi-objective function. As such, it can serve as a useful decision-making supporting tool for distribution network operators. [Projekat Ministarstva nauke Republike Srbije, br. TR33046]