Price (USD) Voltage deviation (p.u) AOA 44.60 29,271 201.28 1606.84 31,123 0.0631 PSO 44.81 29,364 238.50 1617.35 31,264 0.0648 ABC 45.07 30,690 184.33 1560.77 32,480 0.four.5. Comparison Outcomes
Price (USD) Voltage deviation (p.u) AOA 44.60 29,271 201.28 1606.84 31,123 0.0631 PSO 44.81 29,364 238.50 1617.35 31,264 0.0648 ABC 45.07 30,690 184.33 1560.77 32,480 0.4.5. Comparison Outcomes with the AOA with Prior Research The outcomes on the OSPF solved by way of AOA are compared with earlier research as presented in Table 8. In [30], the sizing and placement of renewable power sources using the size of 3 MW are evaluated to minimize the losses and voltage deviation reduction with an ant lion optimizer (ALO). In addition, in [36], the multi-objective optimization of renewable power sources with the size of three MW is studied to minimize the losses and reliability improvement inside the 33-bus distribution PF-06873600 In stock network using the multi-objective hybrid teaching earning optimizer-grey wolf optimization approach (MOHTLBOGWO). The outcomes confirmed the far better overall performance from the OSPF through AOA within the operation of the distribution network compared with all the ALO [36] and MOHTLBOGWO [30] in attaining lower energy loss and more minimum voltage.Table eight. Comparison on the benefits with preceding studies. Item/Method Energy loss (kW) Minimum voltage (p.u) AOA 101.30 0.9561 ALO [36] 103.053 0.9503 MOHTLBOGWO [30] 111.56 0.5. Conclusions Within this paper, the OSPF was presented for the allocation of electric parking lots and wind turbines in a distribution network together with the load following tactic. In the OSPF, the multi-criteria objective function was formulated as the minimization from the power generation expense too as voltage deviation reduction. The optimization variables have been chosen because the location and size on the number of autos in the parking lots and wind resource size within the 33-bus distribution network. The AOA was applied to discover the optimal variables inside the OSPF. The simulations had been implemented in different circumstances of objective functions. The simulation results of your 33-bus distribution network showed that the proposed OSPF determined by the AOA within the third case obtained the lowest energy expense, the minimum price of grid energy, and also the lowest voltage deviation when compared with the situations with no device expenses. The outcomes showed that with the optimal sizing and placement of C2 Ceramide Technical Information theEnergies 2021, 14,20 ofelectric parking lots and optimal contribution of wind resources, the losses and voltage deviations with the electrical network are considerably lowered. Furthermore, depending on the OSPF, purchased energy in the major grid was decreased by injecting energy applying parking lots and wind units into the network. The losses have been reduced from 950.39 kW to 743.33 kW having a 21.78 reduction, the minimum voltage improved from 0.9134 p.u to 0.9561 p.u, as well as the price of grid energy reduced from 3905 kW to 2191 kW in peak load hour with a 43.89 reduction making use of the multi-objective OSPF through the AOA. The optimal sizing and placement of parking lots and renewable power resources using the objective of energy high quality enhancement thinking about uncertainty are suggested for future work.Author Contributions: Conceptualization, S.S. and F.M.; methodology, S.S. and F.M.; application, A.E.-S. and F.M.; validation, F.H.G., A.E.-S. and S.H.E.A.A.; formal analysis, F.H.G., A.E.-S. and S.H.E.A.A.; investigation, S.S. and F.M.; writing–original draft preparation, S.S. and F.M. and a.E.-S.; writing–review and editing, F.H.G., A.E.-S. and S.H.E.A.A.; visualization, S.S. and F.M. All authors have read and agreed to the published version with the manuscript. Funding: The authors received no economic help for.