PEVs data mining based on factor analysis method for energy storage and DG planning in active distribution network: Introducing S2S effect. (15th May 2019) - Ahmadian, Ali; Sedghi, Mahdi; Fgaier, Hedia; Mohammadi-ivatloo, Behnam; Golkar, Masoud Aliakbar; Elkamel, Ali
Abstract: The load demand modeling of Plug-in Electric Vehicles (PEVs) has been taken more attention in today's power system studies. Big-data should be handled for accurate modeling of PEVs load demand. Therefore, the utilization of data mining tools will be helpful for PEVs data analytics and clustering. In this paper, a Factor Analysis (FA) based method is introduced for the PEVs data mining. The load profiles of PEVs that are extracted by Monte Carlo Simulation (MCS) are clustered in some groups optimally using FA method. The clustered data is applied on Energy Storage Systems (ESSs) and Distributed Generation (DGs) planning procedure, separately. The simulation results show the power demand of PEVs effect on both ESSs and DGs planning, however, the temporal feature of PEVs profiles affects only on ESS planning, but not considerably on DG planning. This temporal feature, here called Storage to Storage (S2S) effect, reflects the nature of PEVs and ESS long-term memory which is discussed in this paper. The simulation results show that the optimal ESSs capacity is reduced if the PEVs data are clustered especially in high PEVs penetration. However, the optimal capacities of DGs is the same with and without PEVs data clustering scenarios. Highlights: Factor analysis method is applied for PEVs data mining and uncertainty handling. A new feature, namely Storage to Storage, is introduced for the first time in distribution components planning. The clustered data is applied on both ESSs and DGs planning. The impact of long memory feature of energy storage and PEVs in component planning is investigated.