Day-ahead optimal reactive power ancillary service procurement under dynamic multi-objective framework in wind integrated deregulated power system

Sharma, Akanksha; Jain, Sushma

doi:10.1016/j.energy.2021.120028

Cited by 14 publications

(3 citation statements)

References 45 publications

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“…According to the analysis method of other scholars [23,24], seven independent variables are selected among numerous influencing factors according to the seismic liquefaction data set provided by reference [25,26]. Based on seven characteristic indexes, including intensity𝐼 ′ (𝑋 1 ), groundwater level 𝑑 𝑤 (𝑋 2 ), effective overburden pressure𝜎 0 ′ (𝑋 3 ), blow counts of SPT 𝑁 63.5 (𝑋 4 ), average grain size 𝑑 50 (𝑋 5 ), nonuniformity coefficient 𝐶 𝑢 (𝑋 6 ) and shear-to-stress ratio 𝜏 𝑑 /𝜎 0 ′ (𝑋 7 ), the liquefaction of sandy soil is divided into three grades according to the field conditions.…”

Section: Several Common Assessment Methods Of Sand Liquefactionmentioning

confidence: 99%

Improved artificial electric field algorithm based on multi-strategy and its application

Tian

Liu

Wang

et al. 2022

IJCAI

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Artificial electric field algorithm is a new swarm bionic optimization algorithm, which uses the interaction force of charged particles to create a mathematical model to solve the problem. To improve the global exploration ability and local development ability of artificial electric field algorithms, an artificial electric field algorithm based on opposition learning is proposed. The chaos strategy is used to strengthen the quality of the initial population, and the opposition learning strategy is used to increase the diversity of the population and the development ability of the algorithm. The excellent performance of the algorithm is proved by simulation experiments. The improved artificial electric field algorithm is combined with SVM to construct the sand liquefaction identification model by selecting seven measured indexes, including intensity, underground water level, overlying effective pressure, standard penetration hit number, average particle size, non-uniformity coefficient, and shear stress ratio. Compared with traditional methods such as the standard method and seed simplification method, the results show that the IAEFA-SVM model has high prediction accuracy and provides an effective method for sand liquefaction identification.Povzetek: Predstavljen je izboljšan algoritem umetnega električnega polja na osnovi mnogoterih strategij.

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Section: Several Common Assessment Methods Of Sand Liquefactionmentioning

confidence: 99%

Improved artificial electric field algorithm based on multi-strategy and its application

Tian

Liu

Wang

et al. 2022

IJCAI

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“…44 The optimal planning of 5 salesmen in 400 cities Fig. 45 The optimal planning of 5 salesmen in 500 cities…”

Section: Experiments Settingmentioning

confidence: 99%

“…Its main characteristics include less optimization parameters, rapid convergence in the process of optimization, high accuracy, and low complexity of the algorithm [37]. Since its inception, AEFA has been widely used in continuous optimization problems [38], practical engineering problems [39], image matching problems [40], multi-objective optimization [41], nine parameters triode PV model best estimate [42], cancer detection [43], white blood cells [44], the optimal purchasing problem [45], feature classification [46], assembly line balancing problem [47], and wind turbines active loss in distribution network and the distribution of the voltage deviation problems [48] made a wide range of applications. In this paper, an improved AEFA is proposed.…”

Section: Introductionmentioning

confidence: 99%

Artificial Electric Field Algorithm with Greedy State Transition Strategy for Spherical Multiple Traveling Salesmen Problem

Zhou

et al. 2022

Int J Comput Intell Syst

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The multiple traveling salesman problem (MTSP) is an extension of the traveling salesman problem (TSP). It is found that the MTSP problem on a three-dimensional sphere has more research value. In a spherical space, each city is located on the surface of the Earth. To solve this problem, an integer-serialized coding and decoding scheme was adopted, and artificial electric field algorithm (AEFA) was mixed with greedy strategy and state transition strategy, and an artificial electric field algorithm based on greedy state transition strategy (GSTAEFA) was proposed. Greedy state transition strategy provides state transition interference for AEFA, increases the diversity of population, and effectively improves the accuracy of the algorithm. Finally, we test the performance of GSTAEFA by optimizing examples with different numbers of cities. Experimental results show that GSTAEFA has better performance in solving SMTSP problems than other swarm intelligence algorithms.

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Day-ahead multi-objective procurement of voltage control ancillary service in dynamic wind-solar incorporated deregulated power system

Sharma

Jain

2023

Electr Eng

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Day-ahead optimal reactive power ancillary service procurement under dynamic multi-objective framework in wind integrated deregulated power system

Cited by 14 publications

References 45 publications

Improved artificial electric field algorithm based on multi-strategy and its application

Improved artificial electric field algorithm based on multi-strategy and its application

Artificial Electric Field Algorithm with Greedy State Transition Strategy for Spherical Multiple Traveling Salesmen Problem

Day-ahead multi-objective procurement of voltage control ancillary service in dynamic wind-solar incorporated deregulated power system

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