Optimal Power Flow Using Particle Swarm Optimization of Renewable Hybrid Distributed Generation
The problem of voltage collapse in power systems due to increased loads can be solved by adding renewable energy sources like wind and photovoltaic (PV) to some bus-bars. This option can reduce the cost of the generated energy and increase the system efficiency and reliability. In this paper, a modified smart technique using particle swarm optimization (PSO) has been introduced to select the hourly optimal load flow with renewable distributed generation (DG) integration under different
... different operating conditions in the 30-bus IEEE system. Solar PV and wind power plants have been introduced to selected buses to evaluate theirs benefits as DG. Different solar radiation and wind speeds for the Dammam site in Saudi Arabia have been used as an example to study the feasibility of renewable energy integration and its effect on power system operation. Sensitivity analysis to the load and the other input data has been carried out to predict the sensitivity of the results to any deviation in the input data of the system. The obtained results from the proposed system prove that using of renewable energy sources as a DG reduces the generation and operation cost of the overall power system. limitations and optimize OPF problems effectively. Evolutionary methods include genetic algorithm (GA) [7, 8] , evolutionary programming (EP) , particle swarm optimization (PSO) [10, 11] , simulated annealing (SA) , differential algorithm (DE)  , and shuffle frog leaping algorithm (SFLA)  . These methods have been discussed in details in  . Some researchers have even tried to hybridize the two methods in OPF to improve the performance of the optimization technique to reach the global solution easier and faster. Roy et al.  used a modified SFLA with GA in solving economic load dispatch problems. Other researchers used chaos optimization with linear IM method  . GA with fuzzy logic was also implemented and has been used for OPF solution in  . A hybrid method of PSO, GA and fuzzy logic techniques was also used in OPF  . The allocation of distributed generation (DG) is another interesting area of research. GA was used to optimize the allocation of dispersed generation resources in distribution networks  . A technique for selecting the buses in a sub-transmission system to optimally locate DG has been proposed to reduce transmission losses  which can be translated into reductions in total energy cost. Most OPF studies use systems with constant loads or study a certain load and generation case. Variable loads have been considered in    to study the effect of varying loads or load expansion on the operation of the power system. Integration of renewable energy sources in power systems has many benefits, such as reducing greenhouse emissions, especially CO 2 emissions, and hence assisting in resolving the global warming problem, and reducing the power losses in transmission lines due to power transferring from remote areas. However, the capital cost of renewable power plants is very high compared to conventional power plants, although conversely the operation and maintenance costs are cheaper than those for conventional generation and will continue to decline with recent technical developments. Since most renewable resources are intermittent in nature, it is advantageous to utilize more than one resource when available. Hybridizing renewable resources improves the power system reliability, efficiency and economy. Hybrid renewable energy systems (HRES) can be standalone power systems or integrated with conventional generation      . Economic modeling of HRES was also conducted in  , which proved the feasibility of installing HRES. This paper presents an OPF study of a power system integrated with distributed wind and solar PV as a renewable DG. A modified PSO (MPSO) algorithm is applied to the 30 bus IEEE system with variable load to assess the benefits from using REDG in power systems. The simulation program using MPSO shows the ability to reach the global minimum solution faster and more accurately than with other techniques [30, 31] . The results also show considerable reductions in cost and transmission line losses in case of using renewable energy sources as a DG.