A new rooted tree optimization algorithm for economic dispatch with valve-point effect

Labbi, Yacine; Attous, Djilani Ben; Gabbar, Hossam A.; Mahdad, Belkacem; Zidan, Aboelsood

doi:10.1016/j.ijepes.2016.01.028

Cited by 120 publications

(34 citation statements)

References 34 publications

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“…The EMI is a function of IC, monthly interest rate (r), and payback period (T), as shown in (27). Therefore, the active and reactive power taken from the grid is reduced, which is nothing but the economic savings of DISCO.…”

Section: Payback Period (T)mentioning

confidence: 99%

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Fuzzy logic–based rooted tree optimization algorithm for strategic incorporation of DG and DSTATCOM

Sannigrahi

Ghatak

Acharjee

2019

Int Trans Electr Energ Syst

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Summary In this study, a hybrid approach based on fuzzy logic and rooted tree optimization (RTO) algorithm is proposed for the strategic incorporation of distributed generation (DG) and distributed static compensator (DSTATCOM) in radial distribution system (RDS) to improve voltage profile, reduce losses, and maximize economic and environmental benefits. Fuzzy logic is applied to bring the objectives in the same range, and using RTO technique, the optimal site and size of the devices are determined. Considering the uncertainty in DG's output and time‐varying system load, the proposed technique is tested on 69‐bus and 118‐bus RDSs, and to check the accuracy and feasibility, the simulation results are compared with other existing techniques. The Wilcoxon signed rank test (WSRT) is applied to confirm the superiority of the proposed algorithm. Furthermore, comparative performance analysis is conducted between different planning schemes for facilitating the distribution system planner to select the most suitable one.

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Section: Payback Period (T)mentioning

confidence: 99%

“…27 The roots decide their orientation according to the wetness degree (WD) of the root head. 27 The roots decide their orientation according to the wetness degree (WD) of the root head.…”

Section: Overview Of Rto Algorithmmentioning

confidence: 99%

Fuzzy logic–based rooted tree optimization algorithm for strategic incorporation of DG and DSTATCOM

Sannigrahi

Ghatak

Acharjee

2019

Int Trans Electr Energ Syst

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“…Various optimizat ion techniques as lamda iteration [41], quadratic programming and GAMS [52], differential evolution(DE) [43], teaching learning based optimization(TLBO) [44], Chemical Reaction Optimization (CRO) [45], krill herd algorithm(KHA) [46,53] invasive weed optimization(IW O) [47], gravitational search (GSA ) [48], flower pollination algorith m(FPA) [49], Art ificial Bee Co lony(ABC) [50], rooted tree optimization(RTO) [51] and hybrid DE with particle swarm optimization (PSO) [52] are successfully applied for solution of ELD problem.A co mprehensive review of NI techniques for solution ELD problem is presented in [40] To validate the performance of proposed KHAMCD it is tested on two standards complex cons traint comparative mediu m and large scale ELD problem as below.…”

Section: Economic Load Dispatch Problemmentioning

confidence: 99%

Improved Krill Herd Algorithm with Neighborhood Distance Concept for Optimization

Agrawal¹,

Pandit²,

Dubey³

2016

IJISA

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Abstract-Krill herd algorith m (KHA) is a novel nature inspired (NI) optimization technique that mimics the herding behavior of krill, which is a kind of fish found in nature. The mathematical model of KHA is based on three phenomena observed in the behavior of a herd of krills, which are, mo ment induced by other krill, fo raging motion and random physical d iffusion. These three key features of the algorith m provide a good balance between global and local search capability, which makes the algorith m very powerfu l. The objective is to minimize the distance of each krill fro m the food source and also from the point of highest density of the herd, which helps in convergence of population around the food source. Improvisation has been made by introducing neighborhood distance concept along with genetic reproduction mechanism in basic KH A lgorith m. KHA with mutation and crossover is called as (KHAM C) and KHA with neighborhood distance concept is referred here as (KHAMCD). This paper compares the performance of the KHA with its two imp roved variants KHAMC and KHAM CD. The performance of the three algorithms is compared on eight benchmark functions and also on two real world economic load dispatch (ELD) problems of power system. Results are also compared with recently reported methods to establish robustness, validity and superiority of the KHA and its variant algorithms. Index Terms-Krill Herd A lgorith m (KHA), mutation and crossover, neighborhood distance concept, unimodal function, multimodal function, economic load dispatch.

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“…The first category consists of methods applied in their original version, such as genetic algorithm (GA) [7], particle swarm optimization (PSO) [8], differential evolution (DE) [9], ant colony optimization (ACO) [10], harmony search (HS) [11], artificial bee colony (ABC) [12], teachinglearning-based optimization (TLBO) [13], gravitational search algorithm (GSA) [14], firefly algorithm (FA) [15], biogeography-based optimization (BBO) [16,17], bacterial foraging optimization (BFO) [18], imperialist competitive algorithm (ICA) [19], seeker optimization algorithm (SOA) [20], grey wolf optimization (GWO) [21], backtracking search algorithm (BSA) [22], and root tree optimization (RTO) [23].…”

Section: Introductionmentioning

confidence: 99%

An Improved Particle Swarm Optimization with Biogeography‐Based Learning Strategy for Economic Dispatch Problems

2018

Complexity

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Economic dispatch (ED) plays an important role in power system operation, since it can decrease the operating cost, save energy resources, and reduce environmental load. This paper presents an improved particle swarm optimization called biogeographybased learning particle swarm optimization (BLPSO) for solving the ED problems involving different equality and inequality constraints, such as power balance, prohibited operating zones, and ramp-rate limits. In the proposed BLPSO, a biogeographybased learning strategy is employed in which particles learn from each other based on the quality of their personal best positions, and thus it can provide a more efficient balance between exploration and exploitation. The proposed BLPSO is applied to solve five ED problems and compared with other optimization techniques in the literature. Experimental results demonstrate that the BLPSO is a promising approach for solving the ED problems.

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A new rooted tree optimization algorithm for economic dispatch with valve-point effect

Cited by 120 publications

References 34 publications

Fuzzy logic–based rooted tree optimization algorithm for strategic incorporation of DG and DSTATCOM

Fuzzy logic–based rooted tree optimization algorithm for strategic incorporation of DG and DSTATCOM

Improved Krill Herd Algorithm with Neighborhood Distance Concept for Optimization

An Improved Particle Swarm Optimization with Biogeography‐Based Learning Strategy for Economic Dispatch Problems

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