Rescheduling of real power for congestion management with integration of pumped storage hydro unit using firefly algorithm

Gope, Sadhan; Goswami, Arup Kumar; Tiwari, Prashant; Deb, Subhasish

doi:10.1016/j.ijepes.2016.04.048

Cited by 54 publications

(22 citation statements)

References 20 publications

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“…In the restructured power system, the role of independent system operator (ISO) is to maintain the system security and reliability while making the economical decisions on market participants. In the present paper, the active power outputs of generating units are rescheduled to relieve the congestion in the system [17], [18]. This CM problem is formulated as the minimization of congestion cost.…”

Section: Congestion Management (Cm): Problem Formulationmentioning

confidence: 99%

Optimal power flow based congestion management using enhanced genetic algorithms

Kim

Salkuti

2019

IJECE

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<p>Congestion management (CM) in the deregulated power systems is germane and of central importance to the power industry. In this paper, an optimal power flow (OPF) based CM approach is proposed whose objective is to minimize the absolute MW of rescheduling. The proposed optimization problem is solved with the objectives of total generation cost minimization and the total congestion cost minimization. In the centralized market clearing model, the sellers (i.e., the competitive generators) submit their incremental and decremental bid prices in a real-time balancing market. These can then be incorporated in the OPF problem to yield the incremental/ decremental change in the generator outputs. In the bilateral market model, every transaction contract will include a compensation price that the buyer-seller pair is willing to accept for its transaction to be curtailed. The modeling of bilateral transactions are equivalent to the modifying the power injections at seller and buyer buses. The proposed CM approach is solved by using the evolutionary based Enhanced Genetic Algorithms (EGA). IEEE 30 bus system is considered to show the effectiveness of proposed CM approach.</p>

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Section: Congestion Management (Cm): Problem Formulationmentioning

confidence: 99%

Optimal power flow based congestion management using enhanced genetic algorithms

Kim

Salkuti

2019

IJECE

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“…Diverse optimization methods proposed to ease congestion were explained in detail. In 2016, Sadhan Gope et al [3], proposed an optimization technique for Congestion Management by combining computation of 2 factors like GSF and BSF. Optimal positioning of PSHU was recognized exploiting the BSF's value and amounts of contributor's generators for Congestion Management by rescheduling their outputs was decided to exploit GSF's value.…”

Section: Literature Reviewmentioning

confidence: 99%

Rescheduling Based Congestion Management Method Using Hybrid Grey Wolf Optimization - Grasshopper Optimization Algorithm in Power System

Roy¹

2019

JCMPS

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Safe and incessant power flow is considered as a serious problem which has to be corrected in the transmission line. Actually, rescheduling based Congestion Management is deliberated as the capable solutions for the feature. Still, the technique faces problems based on the cost of rescheduling. A huge number of research studies have been identified hitherto to resolve the problems in Congestion Management. In addition, optimization methods play a very important role to solve this problem. In this case, this work develops a novel rescheduling based Congestion Management. technique that integrates a novel method; Grey Wolf Optimization (GWO) and Grasshopper Optimization Algorithm (GOA) (GWO-GOA) which optimizes generating power of augmented generators using the bus system. The proposed GWO-GOA method is the hybridization of two methods such as GWO and GOA, which aspires to manage the congestion through minimized rescheduling cost. Furthermore, the proposed technique evaluates its performance with the other existing techniques based on rescheduling stratagem regarding the analysis of cost it shows the effectiveness of proposed methods over other existing algorithms.

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“…In the area of clustering and classification, Senthinath et al compared and evaluated the performance of clustering using FA [59]. Gope et al used FA for rescheduling of real power for congestion management concerning pumped storage hydro-units [24]. Long et al used FA for heart disease predictions [40].…”

Section: Applications Of Fa and Its Variantsmentioning

confidence: 99%

Nature-Inspired Algorithms and Applied Optimization

Yang¹

2018

Studies in Computational Intelligence

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Rescheduling of real power for congestion management with integration of pumped storage hydro unit using firefly algorithm

Cited by 54 publications

References 20 publications

Optimal power flow based congestion management using enhanced genetic algorithms

Optimal power flow based congestion management using enhanced genetic algorithms

Rescheduling Based Congestion Management Method Using Hybrid Grey Wolf Optimization - Grasshopper Optimization Algorithm in Power System

Nature-Inspired Algorithms and Applied Optimization

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