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    Investigating the Effectiveness of Wind Turbine and Salp Swarm Optimization in Alleviating Transmission Congestion of Power System
    (Taylor & Francis Inc, 2024) Gautam, Anurag; Nasiruddin, Ibraheem; Sharma, Gulshan; Ahmer, Mohammad F.; Celik, Emre; Bekiroglu, Erdal
    The current power system grapples with congestion challenges arising from technological advancements and deregulation. Conversely, renewable energy sources like wind offer an inexhaustible, cost-effective, and environmentally friendly solution, potentially alleviating congestion in the modern transmission network by reducing the need for conventional generators to reschedule. This article conducts a thorough analysis of how the penetration of wind power impacts congestion costs in conventional energy systems. To address this, a novel approach utilizing the bus sensitivity factor is introduced for precise wind turbine placement. To efficiently mitigate congestion costs, a pioneering Salp Swarm Optimization Algorithm is proposed and validated on a modified IEEE 30 Bus system, demonstrating superior performance compared to other algorithms. The findings underscore the effectiveness of the proposed algorithm and highlight wind turbines, coupled with generator rescheduling, as a potent and cost-effective solution for alleviating transmission network congestion.
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    A novel congestion mitigation key of power system via generator rescheduling using Jaya algorithm
    (Taylor & Francis Inc, 2024) Gautam, Anurag; Nasiruddin, Ibraheem; Sharma, Gulshan; Krishnan, Narayanan; Ahmer, Mohd Faraz; Celik, Emre
    In the contemporary power system, congestion is a big hazard to system security. Congestion reduces the capability of the power system to work under secure limits of thermal and voltage profiles. Implementing FACTS devices or rescheduling the generator's active power output are the most prevalent ways to mitigate congestion in the current scenario and hence this paper presents generator rescheduling for congestion management under the N-1 contingency condition. The generator sensitivity factor (GSF) is used to select the generators' participation in the rescheduling process. The minimization of active power rescheduled by the selected generators is the key to success. JAYA algorithm is used first time in this work to reduce the congestion cost of the system. Furthermore, the JAYA algorithm is applied to a modified IEEE 30 bus system and the results obtained are validated by comparing them with other popular heuristic algorithms as available in the literature for diverse case studies.
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    Sensitivity Factor-Based Congestion Mitigation in DPS Applying Novel Hybrid GWPSO
    (Taylor & Francis Inc, 2023) Gautam, Anubha; Sharma, Parshram; Kumar, Yogendra; Sharma, Gulshan; Gautam, Anurag; Çelik, Emre; Öztürk, Nihat
    In a deregulated power system, with a limited power system framework, alleviation in power transfer has been one of the most crucial problems. This alleviation of bulk power transmission came with congestion, where a transmission line transmits power very near the constrained thermal limits. Congestion has to be mitigated for reliable, economical, and stable operation of the power system. Congestion can be mitigated by applying several methods which may be cost-free or non-cost-free. This article presents a cost-free method employing TCSC as a FACTS device. FACTS devices are very costly. To make the TCSC operation economic, LUF and DLUF are used here to optimally locate the device. TCSC is used here as a variable impedance device. A novel hybrid heuristic optimization technique where Grey Wolf optimizer is merged with Particle Swarm Optimizer to optimize the size of TCSC. The proposed method is implemented to regulate the line reactance for congestion mitigation. The power loss and voltage deviation of the system are reduced by the proposed method to relieve the system congestion. The system security margin is enhanced significantly to make the system more reliable. The proposed algorithm is validated on IEEE 30 bus system and is also validated by comparing the results with parent algorithms. The results reveal that the proposed methodology successfully minimized the objectives for mitigating congestion.