To meet the fast growth of electricity demand, the traditional network solution tends to expand existing substations, build more new substations, and build transmission lines. Distributed Generation (DG) is posed as an alternative method for the network providers not only to accommodate the load increase and relieve network overload, but also to offer other additional technical and economic benefits. This paper addresses the issue of DG planning and has proposed a technique for optimizing the DG size and location to minimize the overall investment and operational cost of the system. The proposed optimization methodology assesses the compatibility of different generation schemes in terms of their cost factors that can be significantly contributed by a DG. The direct and indirect costs of power supply quality, reliability, energy loss, total power operation, and DG investment are used as key cost components of the DG siting and sizing strategy. The Particle Swarm Optimization (PSO) method is applied to obtain the optimal DG planning solutions. Finally, the proposed approach is tested on a distribution feeder of an Australian power network. Simulation results are presented to illustrate the feasibility and effectiveness of the proposed method. Abstract -To meet the fast growth of electricity demand, the traditional network solution tends to expand existing substations, build more new substations, and build transmission lines. Distributed Generation (DG) is posed as an alternative method for the network providers not only to accommodate the load increase and relieve network overload, but also to offer other additional technical and economic benefits. This paper addresses the issue of DG planning and has proposed a technique for optimizing the DG size and location to minimize the overall investment and operational cost of the system. The proposed optimization methodology assesses the compatibility of different generation schemes in terms of their cost factors that can be significantly contributed by a DG. The direct and indirect costs of power supply quality, reliability, energy loss, total power operation, and DG investment are used as key cost components of the DG siting and sizing strategy. The Particle Swarm Optimization (PSO) method is applied to obtain the optimal DG planning solutions. Finally, the proposed approach is tested on a distribution feeder of an Australian power network. Simulation results are presented to illustrate the feasibility and effectiveness of the proposed method.2
The growing demand for power generation makes power systems increasingly more complex to operate and less secure against outages, so the risk of extensive blackouts is growing and needs to be addressed. Cascading failures are the main reason for extensive blackouts, so to investigate this effect a new method including a standard blackout model, named ORNL-PSerc-Alaska (OPA), and static synchronous series compensator (SSSC) placement using an improved particle swarm optimization (IPSO) algorithm is proposed. This study provides two optimization approaches and presents optimal corrective actions. The results will help operators to implement corrective actions like optimal generation and load redispatching to return the system to a stable operating condition. The effectiveness of the proposed model is demonstrated using a realistic transmission network. Simulation results show a significant effect of SSSC on decreasing the risk of cascading failures and the ability of the proposed method to prevent blackout.
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