Wind-Solar-Hydro power optimal scheduling model based on multi-objective dragonfly algorithm

Li, Jie; Lu, Jiming; Yao, Liqiang; Cheng, Liangge; Hui, Qing

doi:10.1016/j.egypro.2019.01.476

Cited by 29 publications

(13 citation statements)

References 10 publications

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“…The number of load requests PR is proportional to all generators' combined capacity, while the transmission line's power losses are ignored [16]. ( 8) A requirement for the optimum distribution from the ith generator of production costs is (9) Where λ is a multiplier of the Lagrange or (10) Determining the value of Pi from the equation above is: (11) The equation above can be iteratively solved. The value of λ is obtained by replacing the value of Pi in equation 7 with equation 8, resulting in the following results: ( 12) or (13) where:…”

Section: F Economical Operation Lambda Iteration Methodsmentioning

confidence: 99%

“…For the delivery of generator loads, the electric power grid's operation is necessary to achieve optimum function. Coordination of the loading of massive electric power produced by the central power plant units needs to be aligned to obtain cheap fuel consumption costs [11] Economic distribution is one means of calculating the quantity of power generated by each generator unit to fulfill a given load by optimally separating the load on the generating units in the system to rationalize the cost of developing fuel consumption [12].…”

Section: B Power Plants Optimizationmentioning

confidence: 99%

“…Ling-Ling Li and others, et al To refine short-term projections for the wind power model [10], the Dragonfly algorithm was used. Jie Li et al To optimize the Wind-Solar-Hydro power scheduling model [11].…”

Section: Introductionmentioning

confidence: 99%

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Optimization Economic and Emissions of Hydro and Thermal Power Plants in 150 kV Systems Using the Dragonfly Algorithm

Wibowo

Handoko

Hermawan

2021

Inf. J. Ilm. Bid. Teknol. Inf. dan Komun.

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Electricity is one of the energies required by daily living since the greater demand for electricity increases greenhouse emissions that create emission gases resulting in global climate change. The main portion of the output cost is fuel's cost to manufacture electrical energy in thermal turbines. The use of electrical energy is currently rising increasingly following the increasing population. The research aims to optimize hydro generation to minimize thermal generation expense and address economic problems and pollution from shipping. With 2016b using Matlab applications and the lambda iteration process, the analysis method uses the Dragonfly Algorithm method. The analysis found that the average cost of fuel consumption provided by the Dragonfly Algorithm method was IDR 151,164,418 per day with an emission of 917.40 tons per day, based on the simulation results the Dragonfly Algorithm in testing by considering the emission of 5 practical steps. Meanwhile, with the emission of 918,044 tonnes per day, the average cost of fuel consumption produced by the Lambda Iteration method is IDR 151,202,209 per day. Test results can enhance the fuel consumption cost of IDR 37,791 and emissions of 0.641 tons with the Dragonfly Algorithm process.

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Section: F Economical Operation Lambda Iteration Methodsmentioning

confidence: 99%

Section: B Power Plants Optimizationmentioning

confidence: 99%

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Optimization Economic and Emissions of Hydro and Thermal Power Plants in 150 kV Systems Using the Dragonfly Algorithm

Wibowo

Handoko

Hermawan

2021

Inf. J. Ilm. Bid. Teknol. Inf. dan Komun.

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“…All the above motivation factors are then mixed up with different weighting factors to make a united factor as below [27]:…”

Section: Improved Optimization Based On Mdamentioning

confidence: 99%

“…Due to the high complexity of the proposed discrete optimization problem, a new optimization algorithm based on the dragonfly algorithm (DA) is developed to manage the optimal scheduling of the units, loads, storages and switches in the microgrid. DA is a new heuristic optimization algorithm that mimics the fly of some insects and makes a powerful algorithm for solving the hard problems [27]. To enrich its performance, a novel self-adaptive modification method is introduced, which makes it possible for dragonflies to select the solution that best matches their structure.…”

Section: Introductionmentioning

confidence: 99%

An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

et al. 2021

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Renewable microgrids are new solutions for enhanced security, improved reliability and boosted power quality and operation in power systems. By deploying different sources of renewables such as solar panels and wind units, renewable microgrids can enhance reducing the greenhouse gasses and improve the efficiency. This paper proposes a machine learning based approach for energy management in renewable microgrids considering a reconfigurable structure based on remote switching of tie and sectionalizing. The suggested method considers the advanced support vector machine for modeling and estimating the charging demand of hybrid electric vehicles (HEVs). In order to mitigate the charging effects of HEVs on the system, two different scenarios are deployed; one coordinated and the other one intelligent charging. Due to the complex structure of the problem formulation, a new modified optimization method based on dragonfly is suggested. Moreover, a self-adaptive modification is suggested, which helps the solutions pick the modification method that best fits their situation. Simulation results on an IEEE microgrid test system show its appropriate and efficient quality in both scenarios. According to the prediction results for the total charging demand of the HEVs, the mean absolute percentage error is 0.978, which is very low. Moreover, the results show a 2.5% reduction in the total operation cost of the microgrid in the intelligent charging compared to the coordinated scheme.

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On the application of Harris hawks optimization (HHO) algorithm to the design of microchannel heat sinks

Abbasi

Firouzi

Şendur

2019

Engineering with Computers

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Wind-Solar-Hydro power optimal scheduling model based on multi-objective dragonfly algorithm

Cited by 29 publications

References 10 publications

Optimization Economic and Emissions of Hydro and Thermal Power Plants in 150 kV Systems Using the Dragonfly Algorithm

Optimization Economic and Emissions of Hydro and Thermal Power Plants in 150 kV Systems Using the Dragonfly Algorithm

An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

On the application of Harris hawks optimization (HHO) algorithm to the design of microchannel heat sinks

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