کنفرانس منطقه ای سیرد

Rearrangement of distribution network with the presence of renewable resources for optimization in a balanced network using IMOPSO algorithm and considering uncertainties by Monte Carlo method

نویسندگان :

Masoud Khajehvand¹

1- شرکت توزیع نیروی برق قزوین

کلمات کلیدی :

Reconfiguration، distributed generation، multi-objective optimization، uncertainty، IMOPSO algorithm

چکیده :

Nowadays, the optimal operation of distribution networks is one of the main priorities of power system operators. The use of distributed generations in distribution networks with proper planning can improve the operation indicators of these networks. Moreover, the operation of a distribution network with an optimal arrangement can promote the technical and economic indicators of the distribution network. In this paper, with combination of two aforementioned issues that is the determination of optimal capacity of distributed generation resources and network reconfiguration, the formulation of a multi-objective optimization problem is resulted. The technical, economic and environmental indicators including losses, voltage deviation, cost, voltage stability index, and pollution have been considered as objective functions. Furthermore, the restrictions such as bus voltage, flow line, maintaining radial structure of network, and distributed generation penetration level are determined as the constraints of the problem, which are required to be observed by each proposed solution. Existing evolutionary algorithms have shown a proper functioning in solving the optimization problem, but its trend is such that eventually propose a global optimal solution. In this paper, a new efficient and powerful multi-objective evolutionary method based on extraction of beam fronts known as IMOPSO is presented by using the density distance operator to maintain the diversity and extent of archive’s solutions as well as using a fuzzy technique to select the best compromise solution to solve the network reconfiguration problem at the same time as determination of optimal capacity of distributed generations. Also, in this paper, the Monte Carlo method is used to model load-induced uncertainties and unpredictability due to production by stochastic scenarios. In this paper, for evaluating the efficiency of proposed formulation, the simulations have been performed on a balanced 33-bus standard network. The simulation results show the efficiency of proposed formulation compared to other algorithms