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

Authors: Didi Omar Elamine; Maria Serraji; El Habib Nfaoui; Jaouad Boumhidi

Addresses: Faculty of Sciences Dhar Mehraz, Department of Computer Science, Sidi Mohammed ben Abdellah University, Fez 30000, Morocco ' Faculty of Sciences Dhar Mehraz, Department of Computer Science, Sidi Mohammed ben Abdellah University, Fez 30000, Morocco ' Faculty of Sciences Dhar Mehraz, Department of Computer Science, Sidi Mohammed ben Abdellah University, Fez 30000, Morocco ' Faculty of Sciences Dhar Mehraz, Department of Computer Science, Sidi Mohammed ben Abdellah University, Fez 30000, Morocco

Abstract: 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.

Keywords: wind energy; MAS; multi-agent systems; neural networks; back-propagation; PSO; particle swarm optimisation; intelligent energy planning; agent-based systems; energy management; smart micro-grid; wind power; wind speed prediction; wind turbines; fuel generators; simulation; JADE.

DOI: 10.1504/IJTIP.2016.074228

International Journal of Technology Intelligence and Planning, 2016 Vol.11 No.1, pp.20 - 35

Received: 27 Nov 2014
Accepted: 26 Jan 2015
Published online: 19 Jan 2016 *

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