Application of Mobile Agent Technology to Power Generation Control in Microgrid Power System

Arai, Junichi; Ishikawa, Motohisa; Yamazaki, Shuichiro; Ito, Toshiyuki

doi:10.1109/appeec.2009.4918184

Cited by 4 publications

(1 citation statement)

References 3 publications

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“…The second is interested in the efficient method for energy resource scheduling of integrated micro-grid with various distributed generators and storage devices (Logenthiran et al, 2010(Logenthiran et al, , 2011Xiong et al, 2012). Besides multi-agent system can also be used to power generation control or restore the power system after a fault (Arai et al, 2009;Solanki et al, 2007). In the smart micro-grid the main energy sources are the renewable energy sources like wind energy, solar energy and biomass but the wind energy is the one with the lowest cost, however the challenge is to integrate this energy in flexible structure like microgrid because this energy depend directly on wind speed that cannot be controlled by any human intervention, in order to guarantee the good performance of different operations the integration of the forecast of wind speed and output power is necessary for this reason there are many proposed works to predict the wind power.…”

Section: Introductionmentioning

confidence: 99%

Multi-agent architecture for optimal energy management of a smart micro-grid using a weighted hybrid BP-PSO algorithm for wind power prediction

Elamine

Serraji

Nfaoui

et al. 2016

IJTIP

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In this paper we present a multi-agent architecture based on wind power prediction using neural network (NN), this process aims to implement smart micro-grid with different generation units like wind turbines and fuel generators. In the proposed architecture this micro-grid can exchange electricity with the main grid therefore it can buy or sell electricity. The main objective is to find the optimal policy using average wind speed prediction for the next hour in order to maximise the benefit and minimise the cost. To forecast the wind speed and taking into account the convergent speed and convergent accuracy, we propose in this paper an NN based on hybrid weighted algorithm combining back-propagation (BP) algorithm with particle swarm optimisation (PSO) algorithm referred to as W-BP-PSO. Finally, for the simulation the Java Agent Development Framework (JADE) platform is used to implement the approach and analyse the results.

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