The penetration of renewable distributed generations (RDGs) into traditional distribution systems (TDSs) remedies many of its deficiencies and shortcomings. Also, it provides mutual technical, economic and environmental benefits for both electricity companies and their customers as well. With a 25% load increase for the standard IEEE 30-bus system, buses 19, 26 and 30 have the lowest voltage magnitudes among all buses. Therefore, these weak buses are selected initially to allocate RDGs. Three cases, namely, one RDG allocated, two RDGs allocated and three RDGs allocated, of RDGs insertion are covered. A novel crow search algorithm auto-drive particle swarm optimization (CSA-PSO) technique is proposed for the first time in this study to specify the optimal allocation, sizing, and number of RDGs based on the total cost and power losses minimization objectives. A new reduction percent formula is used to estimate the reduction in total cost and the total power losses. These will help us to discern between the best cases based on total cost minimization and those based on total power losses minimization to pick up the best among all best cases. In brief, RDGs allocated on buses 19 and 30 is the best among all cases based on total cost reduction and total power losses reduction. Therefore, buses 19 and 30 are recommended to allocate a wind farm and a solar photovoltaic, respectively based on technical and economic issues. Finally, the simulation findings revealed the superiority of the CSA-PSO algorithm in solving the optimal power flow problem with RDGs compared to the state-of-the-art metaheuristic techniques.

SUMMARY This paper presents an optimum sizing methodology to optimize the hybrid energy system (HES) configuration based on genetic algorithm. The proposed optimization model has been applied to evaluate the techno‐economic prospective of the HES to meet the load demand of a remote village in the northern part of Saudi Arabia. The optimum configuration is not achieved only by selecting the combination with the lowest cost but also by finding a suitable renewable energy fraction that satisfies load demand requirements with zero rejected loads. Moreover, the economic, technical and environmental characteristics of nine different HES configurations were investigated and weighed against their performance. The simulation results indicated that the optimum wind turbine (WT) selection is not affected only by the WT speed parameters or by the WT rated power but also by the desired renewable energy fraction. It was found that the rated speed of the WT has a significant effect on optimum WT selection, whereas the WT rated power has no consistent effect on optimal WT selection. Moreover, the results clearly indicated that the HES consisting of photovoltaics (PV), WT, battery bank (Batt) and diesel generator (DG) has superiority over all the nine systems studied here in terms of economical and environmental performance. The PV/Batt/DG hybrid system is only feasible when wind resource is very limited and solar energy density is high. On the other hand, the WT/Batt/DG hybrid system is only feasible at high wind speed and low solar energy density. It was also found that the inclusion of batteries reduced the required DG and hence reduced fuel consumption and operating and maintenance cost. Copyright © 2014 John Wiley & Sons, Ltd.

Hybrid energy systems (HESs) comprising photovoltaic (PV) arrays and wind turbines (WTs) are remarkable solutions for electrifying remote areas. These areas commonly fulfil their energy demands by means of a diesel genset (DGS). In the present study, a novel computational intelligence algorithm called supply-demand-based optimization (SDO) is applied to the HES sizing problem based on long-term cost analysis. The effectiveness of SDO is investigated, and its performance is compared with that of the genetic algorithm (GA), particle swarm optimization (PSO), gray wolf optimizer (GWO), grasshopper optimization algorithm (GOA), flower pollination algorithm (FPA), and big-bang-big-crunch (BBBC) algorithm. Three HES scenarios are implemented using measured solar radiation, wind speed, and load profile data to electrify an isolated village located in the northern region of Saudi Arabia. The optimal design is evaluated on the basis of technical (loss of power supply probability [LPSP]) and economic (annualized system cost [ASC]) criteria. The evaluation addresses two performance indicators: surplus energy and the renewable energy fraction (REF). The results reveal the validity and superiority of SDO in determining the optimal sizing of an HES with a higher convergence rate, lower ASC, lower LPSP, and higher REF than that of the GA, PSO, GWO, GOA, FPA, and BBBC algorithms. The performance analysis also reveals that an HES comprising PV arrays, WTs, battery banks, and DGS provides the best results: 238.