Smart Distribution Network Reconfiguration Based on Optimal Planning of Distributed Generation Resources Using Teaching Learning Based Algorithm to Reduce Generation Costs, Losses and Improve Reliability

摘要

Over the past years, the goals of planning power systems have changed. Use of high-efficiency resources, loss reduction, environmental issues, projects' costs, etc. are among these goals. There are various ways in the power system to improve the conditions that can be referred to as the reconfiguration of distribution feeders, minimizing actual losses and improving reliability. On the other hand, in order to achieve the stated goals, distributed generation sources enter distribution networks, which are mostly radial, with a high penetration ratio. The presence of these resources causes problems in distribution networks. Therefore, reconfiguration operation comes with difficulty due to the network complication. Moving networks towards smart ones makes the data available at any moment and eliminates uncertainties. So, there is the possibility of reconfiguration in the smart space instantly. This paper has focused on network reconfiguration and simultaneous planning of generation rate of distributed generation sources per hour, considering the 24-hour load profile in order to minimize the costs of network losses and energy not supplied. Also, given the fact that distributed generation sources are used for costs minimization, network owners must pay for distributed generation sources. Therefore, this cost is also considered in 24 hours for the proposed target function. To do this, the optimal location of distributed generation source is identified and then reconfiguration and generation planning are done in 24-hours. Simulations are performed on the standard IEEE 69-bus distribution network that confirms the effectiveness of the proposed method.