Microgrids are defined as an interconnection of several renewable energy sources in order to provide the load power demand at any time. Due to the intermittence of renewable energy sources, storage systems are necessary, and they are generally used as a backup system. Indeed, to manage the power flows along the entire microgrid, an energy management strategy (EMS) is necessary. This paper describes a microgrid energy management system, which is composed of solar panels and wind turbines as renewable sources, Li-ion batteries, electrical grids as backup sources, and AC/DC loads. The proposed EMS is based on the maximum extraction of energy from the renewable sources, by making them operate under Maximum Power Point Tracking (MPPT) mode; both of those MPPT algorithms are implemented with a multi-agent system (MAS). In addition, management of the stored energy is performed through the optimal control of battery charging and discharging using artificial neural network controllers (ANNCs). The main objective of this system is to maintain the power balance in the microgrid and to provide a configurable and a flexible control for the different scenarios of all kinds of variations. All the system’s components were modeled in MATLAB/Simulink, the MAS system was developed using Java Agent Development Framework (JADE), and Multi-Agent Control using Simulink with Jade extension (MACSIMJX) was used to insure the communication between Simulink and JADE.

This paper proposes a multi-agent system for energy management in a microgrid for smart home applications, the microgrid comprises a photovoltaic source, battery energy storage, electrical loads, and an energy management system (EMS) based on smart agents. The microgrid can be connected to the grid or operating in island mode. All distributed sources are implemented using MATLAB/Simulink to simulate a dynamic model of each electrical component. The agent proposed can interact with each other to find the best strategy for energy management using the java agent development framework (JADE) simulator. Furthermore, the proposed agent framework is also validated through a different case study, the efficiency of the proposed approach to schedule local resources and energy management for microgrid is analyzed. The simulation results verify the efficacy of the proposed approach using Simulink/JADE co-simulation.

Abstract:The purpose of this paper is to ensure a tracking control of output state especially the active and reactive powers of doubly fed induction generator (DFIG) by using the approach of Parallel Distributed Compensation (PDC) of the fuzzy control type Takagie-Sugeno (T-S) which determines the control laws containing fuzzy tracking and return state. This tracking has been built to converge the output vectors of the DFIG to a desired state. In this work, the quadratic function of lyaponov and a linear matrix inequality (LMI) are used to obtain the gains of the tracking control and the controller.The simulations results of law control based on the tracking and controller gains allow the active and reactive powers obtained must follow the reference proposed.

This paper aims to ensure a stability and observability of doubly fed induction generator DFIG of a wind turbine based on the approach of fuzzy control type T-S PDC (Parallel Distributed Compensation) which determines the control laws by return state and fuzzy observers. First, the fuzzy TS model is used to precisely represent a nonlinear model of DFIG proposed and adopted in this work. Then, the stability analysis is based on the quadratic Lyapunov function to determine the gains that ensure the stability conditions. The fuzzy observer of DFIG is built to estimate non-measurable state vectors and the estimated states converging to the actual statements. The gains of observatory and of stability are obtained by solving a set of linear matrix inequality (LMI). Finally, numerical simulations are performed to verify the theoretical results and demonstrate satisfactory performance. Keyword: Copyright © 2016 Institute of Advanced Engineering and Science.All rights reserved. Corresponding Author:Fouad Abdelmalki, Department of Electrical, Hassan 1er University, Faculty of Sciences and Technology, B.P: 577, 26000 Settat, Morocco. Email: f.abdelmalki@gmail.com INTRODUCTIONThe doubly fed induction generator has been popular because of its higher energy transfer capability, low investment and flexible control [1]. The control of the DFIG is well known to be difficult owing to the fact that the dynamic model is nonlinear and some states cannot be measured. For this, it is important to know the evolution of the state of the nonlinear system (DFIG).A considerable research has been done on the modeling and control of DFIG [2]- [8]. For monitoring, decision making and feedback control of the DFIG, very interesting approach were done in the fuzzy modeling and control, especially with Takagi-Sugeno (T-S) fuzzy [9] and related parallel distributed compensation (PDC) control algorithm [10].The Takagi-Sugeno (TS) fuzzy modeling framework with parallel-distributed compensation (PDC) technique [11] offers a viable way to control and approximate a wide class of nonlinear dynamical systems [12] by providing a generic nonlinear state-space model. To ensure global system stability and observability of DFIG, a quadratic Lyapunov function common to all subsystems is found by solving a set of linear matrix inequalities LMIs [10], [13]. Then using powerful computational tool boxes, such as Matlab LMI Toolbox. We obtain the controller and observers gains for local fuzzy models. This paper is organized as follows. In Section II, the dynamic model of doubly fed induction generator is presented. In Section III, study of T-S fuzzy modelling, method PDC and Fuzzy state observer.

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