Fuzzy PSO Congestion Management using Sensitivity-Based Optimal Active Power Rescheduling of Generators

Venkaiah, Ch.; Kumar, D. M. Vinod

doi:10.5370/jeet.2011.6.1.032

Cited by 13 publications

(5 citation statements)

References 25 publications

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“…As a real test system, the Indian 75-bus system including 15 generator buses and 60 load buses is selected while its data is available in [13]. Bus 12 has been assigned as the Slack bus.…”

Section: Practical Indian 75-bus Systemmentioning

confidence: 99%

“…The optimization algorithms are employed to optimally reschedule the active power of the selected generators for relieving congestion in line 71. Table 6 gives the active power generation of the 10 participating generators before and after CM using MVCS, PSO [13] , FPSO [13] , and FDR-PSO [13] . [13] FPSO [13] FDR-PSO [13] The optimization algorithms have been executed over five times on the studied test system to find out the robustness and effectiveness of the MVCS.…”

Section: Practical Indian 75-bus Systemmentioning

confidence: 99%

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Congestion Management in Deregulated Electricity Market with Generator Sensitivity Effects

Ghasemi

2019

EST

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This paper proposes a novel modified virus colony search (MVCS) algorithm to solve congestion management (CM) problem in the deregulated electricity market considering generator sensitivity and rescheduling of active powers. Thus, the generator output has been recognized by generator sensitivity index to manage transmission line limitation so as to get maximum efficiency in power transmission. The CM model is formulated based on a single objective function to minimize final cost by optimal planning of active powers responding to generators. To develop the exploration and exploitation of VCS, proposes some modifications into local and global parts which they make the best stability in VCS structure. To reveal the MVCS performance, it is assessed on three well-known power systems, IEEE 30-bus and IEEE 118-bus, which the numerical results show acceptable performance compare to other available methods.

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“…As a real test system, the Indian 75-bus system including 15 generator buses and 60 load buses is selected while its data is available in [13]. Bus 12 has been assigned as the Slack bus.…”

Section: Practical Indian 75-bus Systemmentioning

confidence: 99%

Section: Practical Indian 75-bus Systemmentioning

confidence: 99%

Congestion Management in Deregulated Electricity Market with Generator Sensitivity Effects

Ghasemi

2019

EST

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“…Farklı tıkanıklık senaryolarında yapılan benzetim çalışmalarına göre, önerilen hibrit algoritmadan elde edilen sonuçlar FFA algoritmasının sonuçları ile karşılaştırılmış ve önerilen algoritma optimal çözüme yakınsamada daha başarılı olmuştur. Tıkanıklık yönteminde toplam tıkanıklık maliyetinin minimizasyonu için literatürde; IDE [12], MO-GSO [13], FPSO [14], GSA [15], ve ICSA [16] gibi farklı algoritmalar kullanılmıştır. Bu çalışmada, SMA [17] ve TDO [18] algoritmaları değiştirilmiş IEEE 30 bara test sisteminde iki farklı tıkanıklık senaryosu altında toplam tıkanıklık maliyetinin minimizasyonu için kullanılmıştır.…”

Section: Introductionunclassified

Slime Mould Optimization Algorithm for Solving Congestion Management Problem in Modern Power Systems

UĞUR,

DUMAN

2023

Mühendislik Bilimleri Ve Araştırmaları Dergisi

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Günümüzde, yeniden yapılandırılmış elektrik güç sistemlerinde meydana gelen belirsizlikler rekabetçi elektrik piyasasında tıkanıklık oluşturmakta olup, bu durum güç sisteminde ekonomik kayba ve sistem güvenilirliğini zayıflatmaktadır. Bu durum göz önüne alındığında, tıkanıklık yönetimi (CM) modern güç sistemlerinin işletilmesi ve kontrolü açısından en önemli planlama problemlerden biri olarak ortaya çıkmaktadır. Bu makalede, tıkanıklık yönetimi problemini çözmek için son zamanlarda literatüre sunulan Balçık Küfü Algoritması (SMA) ve Tazmanya Canavarı Optimizasyon (TDO) algoritması kullanılmıştır. SMA ve TDO algoritmaları, generatörlerin ürettikleri aktif güç değerlerini yeniden planlayarak elektrik piyasasında meydana gelen tıkanıklığı belirtilen eşitlik ve eşitsizlik kısıtlamaları içerisinde hafifletmektedir. Ayrıca önerilen algoritmalar, toplam yeniden planlama maliyetini minimize ederken, benzetim çalışmalarında oluşturulan senaryolara göre meydana gelen tıkanıklığın giderilmesini sağlamaktadır. Benzetim çalışmaları değiştirilmiş IEEE 30- bara test sisteminde gerçekleştirilmiş ve elde edilen sonuçlar literatürdeki diğer sonuçlar ile karşılaştırılmıştır. Karşılaştırma sonuçlarına göre, SMA algoritmasının tıkanıklık yönetimi problemini çözmede literatürdeki diğer algoritmalardan daha başarılı olduğu görülmüştür.

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“…The overloads in a transmission network were alleviated by generation rescheduling. In [13], the authors' proposed a fuzzy particle swarm optimization (FPSO) based congestion management by optimal rescheduling of active powers of generators. The generators had been chosen based on the generator sensitivity to the congested line.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy ranking based non-dominated sorting genetic algorithm-II for network overload alleviation

Pandiarajan¹,

Babulal²

2014

Archives of Electrical Engineering

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This paper presents an effective method of network overload management in power systems. The three competing objectives 1) generation cost 2) transmission line overload and 3) real power loss are optimized to provide pareto-optimal solutions. A fuzzy ranking based non-dominated sorting genetic algorithm-II (NSGA-II) is used to solve this complex nonlinear optimization problem. The minimization of competing objectives is done by generation rescheduling. Fuzzy ranking method is employed to extract the best compromise solution out of the available non-dominated solutions depending upon its highest rank. N-1 contingency analysis is carried out to identify the most severe lines and those lines are selected for outage. The effectiveness of the proposed approach is demonstrated for different contingency cases in IEEE 30 and IEEE 118 bus systems with smooth cost functions and their results are compared with other single objective evolutionary algorithms like Particle swarm optimization (PSO) and Differential evolution (DE). Simulation results show the effectiveness of the proposed approach to generate well distributed pareto-optimal non-dominated solutions of multi-objective problem.

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Fuzzy PSO Congestion Management using Sensitivity-Based Optimal Active Power Rescheduling of Generators

Cited by 13 publications

References 25 publications

Congestion Management in Deregulated Electricity Market with Generator Sensitivity Effects

Congestion Management in Deregulated Electricity Market with Generator Sensitivity Effects

Slime Mould Optimization Algorithm for Solving Congestion Management Problem in Modern Power Systems

Fuzzy ranking based non-dominated sorting genetic algorithm-II for network overload alleviation

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