Solving smooth and non-smooth economic dispatch using water cycle algorithm

Dihem, A.; Salhi, A.; Naimi, Djemai; Bensalem, Ahmed

doi:10.1109/icee-b.2017.8192083

Cited by 10 publications

(6 citation statements)

References 21 publications

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“…In [54], the dynamic ELD problem was solved by modified TLBO. In [55], smooth as well as non-smooth ELD was solved by WCA. The ELD problem in presence of wind power was attacked by hybrid BA [56].…”

Section: Related Workmentioning

confidence: 99%

“…VOLUME 4, 2016 ELD, ELD with valve point effect, CEED CTO [28] Dynamic ELD PSO TCO [29] ELD considering renewable sources Modified BA [30] ELD with losses QBA [31] Dynamic ELD considering renewable sources PSO [32] ELD with valve point effect QBA [33] ELD with losses PSO [34] Non convex ELD ACS [35] CEED Hybrid FA BA [74] ELD Jaya Algorithm [36] ELD with valve point effect, CEED MBA [37] ELD with valve point effect, ramp rates and prohibited zones Improved PSO [38] Convex and non-convex ELD SHO [39] ELD with ramp rates and prohibited zones WOA [40] Convex, non-convex and dynamic ELD MFO [41] ELD with valve point effect Hybrid GWO [42] ELD with losses Improved PSO [43] ELD with losses AEFA [44] Non-convex ELD ALO [45] ELD with losses ACSS [46] ELD with losses Adaptive PSO [47] ELD with losses Enhanced PSO [48] ELD with losses GWO [49] ELD with losses Enhanced BA [50] CEED QBA [51] Dynamic ELD, CEED Modified ABC [52] CEED Enhanced Cultural algorithm [53] Dynamic CEED Chaotic FA [54] Dynamic CEED TLBO [55] Smooth and non-smooth ELD WCA [56] ELD with renewable resources Hybrid BA [57] Multi-area ELD SSA [75] Multi-area ELD Hybrid Jaya TLBO [58] Multi-area ELD ISFS [76] CEED with renewable sources MOEA/D [59] Multi-area ELD MFO [60] CEED Improved TLBO [61] ELD with losses Modified GWO [62] Dynamic ELD GA PSO…”

Section: Economic Load Dispatch Problemmentioning

confidence: 99%

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Recent Methodology-Based Gradient-Based Optimizer for Economic Load Dispatch Problem

et al. 2021

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Economic load dispatch (ELD) in power system problems involves scheduling the power generating units to minimize cost and satisfy system constraints. Although previous works propose solutions to reduce CO2 emission and production cost, an optimal allocation needs to be considered on both cost and emission-leading to combined economic and emission dispatch (CEED). Metaheuristic optimization algorithms perform relatively well on ELD problems. The gradient-based optimizer (GBO) is a new metaheuristic algorithm inspired by Newton's method that integrates both the gradient search rule and local escaping operator. The GBO maintains a good balance between exploration and exploitation. Also, the possibility of the GBO getting stuck in local optima and premature convergence is rare. This paper tests the performance of GBO in solving ELD and CEED problems. We test the performance of GBO on ELD for various scenarios such as ELD with transmission losses, CEED, CEED with valve point effect, and other various test networks. The experimental results revealed that GBO has been obtained better results compared to eight other metaheuristic algorithms such as Slime mould algorithm (SMA), Elephant herding optimization (EHO), Monarch butterfly optimization (MBO), Moth search algorithm (MSA), Earthworm optimization algorithm (EWA), Artificial Bee Colony (ABC) Algorithm, Tunicate Swarm Algorithm (TSA) and Chimp Optimization Algorithm (ChOA). Therefore, the simulation results showed the competitive performance of GBO as compared to other benchmark algorithms.INDEX TERMS Gradient-Based Optimizer (GBO); Economic Load Dispatch (ELD); Combined Economic and Emission Dispatch (CEED); Metaheuristics; Optimization.

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Section: Related Workmentioning

confidence: 99%

Section: Economic Load Dispatch Problemmentioning

confidence: 99%

Recent Methodology-Based Gradient-Based Optimizer for Economic Load Dispatch Problem

et al. 2021

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“…Streams flow into the rivers or the sea through a distance (d) as illustrated in Figure 4, and Equation (27).…”

Section: Population Initializationmentioning

confidence: 99%

“…The second strategy is based on the Intelligent Optimization Scenario (IOS) which utilizes the Water Cycle Optimization Technique (WCOT) [26] compared to Genetic Algorithm (GA). The Water Cycle Algorithm (WCA) efficiency has been proved in solving complex issues with the optimum solution compared to other optimization techniques like linear programing (LP), nonlinear programming (NLP) and practical swarm optimization (PSO) [27] The paper is divided into five main sections. Section 2 illustrates the operation conditional flags and multi-objective indices for decision-making criteria.…”

Section: Introductionmentioning

confidence: 99%

Smart Hybrid Micro-Grid Integration for Optimal Power Sharing-Based Water Cycle Optimization Technique

et al. 2018

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“…There has been continuous and sustained effort to develop new heuristic optimization methods inspired by the behaviour of natural organism and plants, natural occurrences and various laws of science. Good recent examples include immune algorithm [30], lightning flash algorithm [31] teaching-learning-based optimization algorithm [32], symbiotic organism search algorithm [33], enhanced firework algorithm [34], modified differential evolution [35], adaptive charged system search algorithm [36], distributed auction optimization algorithm [37], water cycle algorithm [38] and mine blast algorithm [39]. Development of new hybrid methods such as the immune evolutionary programming has also grown [40].…”

Section: Introductionmentioning

confidence: 99%

Filter feeding allogenic engineering optimization algorithm for economic dispatch

Mutegi¹,

Nwulu²

2021

Int. J. EQ

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The main objective of the economic dispatch problem in a power system is to minimize the total thermal fuel cost of the committed generators while satisfying the various system equality and inequality operational constraints. This research developed a new optimization algorithm, named the filter feeding allogenic engineering algorithm, for use in solving the economic dispatch problem. This meta-heuristic algorithm has been inspired by the filter feeding and motile behaviour of allogenic engineers. The newly developed algorithm was formulated using the Matlab software environment, and its performance was tested using the IEEE 39-Bus, 10-Generator system. A comparative analysis was also conducted with the Ant lion optimization heuristic algorithm, and the obtained results indicate that the filter feeding allogenic engineering algorithm yields superior performance.

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Solving smooth and non-smooth economic dispatch using water cycle algorithm

Cited by 10 publications

References 21 publications

Recent Methodology-Based Gradient-Based Optimizer for Economic Load Dispatch Problem

Recent Methodology-Based Gradient-Based Optimizer for Economic Load Dispatch Problem

Smart Hybrid Micro-Grid Integration for Optimal Power Sharing-Based Water Cycle Optimization Technique

Filter feeding allogenic engineering optimization algorithm for economic dispatch

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