Affiliations: [a]
Department of Computer and Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| [b]
Department of Electrical and Electronics Engineering, Shiraz University of Technology, Shiraz, Iran
| [c] Faculty of Electrical Engineering, K.N. Toosi University of Technology, Tehran, Iran
Correspondence:
[*]
Corresponding author. Soodabeh Soleymani, Department of Computer and Electrical Engineering, Science and
Research Branch, Islamic Azad University, Tehran, Iran. E-mail: s.soleymani@srbiau.ac.ir.
Abstract: In order to investigate the effect of Distribution Automation (DA) on network reliability enhancement, two different planning and operation strategies are presented in this paper i.e. switch placement and Distribution Feeder Reconfiguration (DFR). Furthermore, because of increased utilization of Plug-in Electric Vehicles (PEVs) and their capability of Vehicle to Grid (V2G) technology, these intelligent bilateral energy sources can affect on switch placement problem and system reliability. Therefore, through a new failure rate reduction approach due to PEVs as well as evaluating of the System Average Interruption Frequency Index (SAIFI) and System Average Interruption Duration Index (SAIDI), system reliability is investigated in both strategies. In this regard, the objective functions to be investigated are total cost of switches and cost of PEV fleets in switch placement problem as well as SAIFI, SAIDI, active power loss and voltage deviation in DFR problem. In addition, in order to achieve more reliable results, both of strategies are presented in stochastic framework by 2m + 1 Point Estimate Method (PEM). The uncertainty of the active and reactive loads and load annual growth rate as well as active and reactive hourly loads are considered in switch placement and DFR problems, respectively. Simulation results verify the proposed strategies should be investigated in probabilistic framework and finally feasibility of the proposed method is studied by Modified Teaching Learning Algorithm (MTLA) through a 69 bus IEEE distribution test system.
Keywords: Distribution automation, reliability enhancement, PEV fleets, new failure
rate reduction approach, 2m + 1 PEM
