Multi-agent system based sequential energy management strategy for Micro-Grid using optimal weighted regularized extreme learning machine and decision tree

Abstract

Renewable energies are fundamentally changing the traditional power grid. Their integration in micro grid constitutes the best way to produce clean energy in a large scale. However, classical control methods based centralized approaches are not efficient to manage and control the different operatio ns in micro grid. In this paper, an intelligent energy management system is presented for micro grid power control based on the distributed paradigm of Multi-Agent System. Its main objective is to find the optimal control of a MG with grid-connected mode in order to control the amount of power delivered or taken from the Distribution Network so as to minimize the cost and maximize the benefit. We present also a photovoltaic and wind power prediction method using an Optimal Weighted Regularized Extreme Learning Machine algorithm in which the Particle Swarm Optimization method is used to optimize the regularization parameter. The algorithm is tested on real weather data and has shown a good generalization performance and better results than the basic Extreme Learning Machine algorithm while keeping its extremely fast training speed ability. To establish an efficient energy management strategy, a Decision Tree is used to ensure the availability of power on demand by taking a reasonable decision about charging batteries/selling electricity and discharging batteries/buying electricity in order to reduce the balance between cost and benefit.