Optimal Allocation of Renewable Energy Sources in Electrical Power System Using Oscillatory Particle Swarm Optimization

The integration of distributed generators (DGs) into distribution networks in optimal allocation is one of the main issues facing power system engineers to ensure improved stability and economic operation. This article presents a detailed analysis of the impacts of the optimal allocation and the number of DGs on both system steady-state and transient performances of distribution networks. An oscillatory particle swarm optimization (OPSO) algorithm was used to find the optimal allocations of DGs via minimizing various objective functions that deal with Total Transmission Losses, Voltage Regulation, and Power Performance Index. The OPSO is used to optimize these functions as a single and as a multiobjective optimization problem. The effectiveness of the method is demonstrated with the IEEE-14 bus as an example of distribution networks with a 50% increase in system loading. Two penetration scenarios have been considered, the optimal sizes and locations of DGs are obtained, and the results are presented.In addition, the impact of the penetration level of Photovoltaic and Wind Energy sources on transient performance is obtained using detailed nonlinear models of both synchronous machines and DGs sources. The system response is then obtained when the system is subjected to a three-phase short circuit fault for six cycles and the results are presented in a comparative form for different penetration levels. The techniques and results presented in the article form a useful base for power system engineers in planning and operating distribution systems with high penetration levels of RDG sources. K E Y W O R D Sconcentrated and distributed DG (C-DG, D-DG), distributed generation (DG), optimal DG allocations, oscillatory PSO, penetration level.

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“…More details about the OPSO parameters and updates are given elsewhere . The effectiveness of using the OPSO is demonstrated elsewhere …”

Section: Opso Implementationmentioning

confidence: 99%

“…The results illustrate that both techniques give the same results of the optimization process. Therefore, the OPSO is chosen for this work due to its fast convergence and other advantages …”

Section: Opso Implementationmentioning

confidence: 99%