Optimal Reconfiguration in Smart Distribution Network in the Presence of DG Resources under Uncertainties

Due to the growth and expansion of cities and the unchanged distribution network, an important issue for electricity distribution companies is customer satisfaction with the quality of received power. It is clear that with the expansion of load centers, the amount of line power flow increases, that creates problems such as voltage drop and increased losses. Distribution network reconfiguration is one of the solutions for network administrators to prevent these problems. With increasing power consumption, environmental issues, and government constraints for power generation, have raised challenges in the distribution system. The solution is a smart grid implementation. In this paper are considered reconfiguration, distributed resources (PV and Wind systems) and energy storage systems (ESS) to achieve smart grid. The effect of scattered products on the reduction of losses, the effect of capacitive banks on improving the voltage profile and reducing losses, and the problem of reconfiguration on the performance indicators of the distribution system and the reduction of losses are investigated and proposed as a multi-objective function. The objective functions used include reducing total voltage deviations, reducing total system losses, increasing the security margin of lines, reducing the energy purchased from the main network, and reducing the energy variance from the main network. Several scenarios for examining the methods for improving the target functions have been investigated and the simulation results have been shown. The use of reconfiguration has a significant impact on the losses and voltage profiles, and distributed sources also have a positive impact on all sectors and affect the power of the network. With the activation of ESSs in the smart distribution network, the economic objective function is reduced and the grid has the ability to sell electricity to the main network. In the future work, the linearization of the proposed model and mathematical optimization methods can be used.