Luis I Lugo-Villeda
email: l.lugovilleda@sssup.it

Antonio Frisoli
email: a.frisoli@sssup.it

Edoardo Sotgiu

Giovanni Greco

Massimo Bergamasco
email: massimo.bergamasco@sssup.it

Right-Arm Robotic-Aided-Therapy with the Light-Exoskeleton: A General Overview

Keywords: Exoskeletons, Robotic-Aided-Rehabilitation, Emerging Technologies Integration, Robotic Constrained Therapy

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Robotic-Aided-Rehabilitation is one of the most multidisciplinary emerging fields that has been studied for several decades; it is due to the synergetic ability to use the interaction of robots into the disable humans-workspace as part of the rehabilitation robotics realm [START_REF] Prange | Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke[END_REF]. Actually, rehabilitation robotics belongs to the so-called program Health Workforce of the WHO (World Health Organization) wherein this technology is one of the forces driving such a program [START_REF] Burdea | Keynote address: Virtual rehabilitation-benefits and challenges[END_REF]. According to U.S. Centers for Disease Control and Prevention and the American Heart Association [START_REF] Organization | The world health report 2006: working together for health[END_REF], 80,000,000 American adults (one in three) have one or more types of cardiovascular disease (CVD), of whom 38,100,000 are estimated to be age 60 or older, this represents as total direct and indirect amount of CVD for 2008 of $475.3 billion. Similar conditions are prevailing in Italy and EU countries; therefore, boosting the application of rehabilitation robotics for assisting people to recover their normal daily-living skills is quite essential. But what does rehabilitation robotics involve? The meaningful answer in our context leads to define it as follows:

Rehabilitation Robotics is the multi-disciplinary field which concerns with restoring a disable person to an acceptable level of whether physical, mental or even social interaction with him/her environment by means of applied robotics, emerging technologies and health care areas.

This definition helps to assess the robotic-mediated-therapy as an area of rehabilitation robotics, offering the capabilities to improve the gradual outcome of post-stroke patients engaged on rehabilitation of neuromotor control in comparison with conventional therapy [START_REF] Lugo-Villeda | Robust tracking of the light-exoskeleton for arm rehabilitation tasks[END_REF]. Also the idea of robotic-mediated-therapy is an opportunity to innovate and improve trough new emerging fields the rehabilitation endeavour field. Here, we present some advantages by using our upper limb rehabilitation demonstrator in comparison with other approaches.

Contribution to Technological Innovation

Despite of these impressive achievements regarding exoskeletons, it has been said, they are in the early stage due to the involved fields that need being enhanced [START_REF] Lugo-Villeda | Regressor-free force/position control of fixed-base exoskeletons for rehabilitation tasks[END_REF]. Nevertheless, as long as we unify important concepts with their associated technology, new trends along the technological innovation stream are arising for exploiting at maximum the application of such devices into rehabilitation fields.

Particularly, two of the foremost goals are presented as innovative:

1. The Robotic-Constrained-Therapy (RCT) which has been conceptualised as the application of constrained kinematics and constrained dynamics into a framework for treating the problem of upper limbs plasticity at the level of arm's joints. 2. The inclusion of nonlinear controllers that our best knowledge, they are practically unknown, but those present important characteristics managing the total energy of the closed-loop to be passive in every time and controlling the parametric and non parametric uncertainties induced by the patient's tremor.

Contribution to Technological Innovation Trough Emerging Fields.

The contribution of this article is three-fold:

1. A robotic-aided-rehabilitation platform is generally presented under the rehabilitation robotics philosophy. 2. A general landscape of the novelty robotic constrained therapy is introduced. 3. Preliminary results are promising, such that the proposed platform is ready to undergo to real disable patients at the Cisanello hospital, Pisa, Italy, as the immediate future work.

Organization

The outline of this contribution is organized as follows. Section 3 shows the actual work into this field. The proposed system under the robotics rehabilitation trends is given into Section 4. Besides this a set of experimental results on healthy people is shown into Section 5. Section 6 presents, a set of discussions about the obtained results. Finally the conclusions and future work are given in Section 7.

Related Work

We describe the actual work driven by two major key approaches [START_REF] Reinkensmeyer | Guidance-based quantification of arm impairment following brain injury: a pilot study[END_REF]:

Fixed-Frame Exoskeletons

In the first classification, we have the Armin II, an exoskeleton for human-arm rehabilitation whose 6 DoF have been used in conjunction with virtual environments, suitable for helping post-stroke patients. The exoskeleton uses a simple PD plus gravity compensation controller to obtain apparently interactive forces, which are computed on the basis of the virtual environment interaction by an impedance controller [START_REF] Nef | Armin -exoskeleton for arm therapy in stroke patients[END_REF]. A 7-DoF upper-limb exoskeleton which can be used for therapeutic diagnostics and for physiotherapy or as a haptic device in virtual reality simulation is presented in [START_REF] Perry | Design of a 7 degree-of-freedom upper-limb powered exoskeleton[END_REF], however it does not introduces neither the techniques for doing rehabilitation nor the control scheme for assuring stability1 .

End-Effector Devices

In the second category, we have more devices to analyse, but all of them are very competitive. Mostly of end-effector devices have been gone under the commercial track, that is the case of the MIT Manus [START_REF] Krebs | Overview of clinical trials with mit-manus: a robot-aided neuro-rehabilitation facility[END_REF], A pantograph-based manipulator which works into 2-D, it is constituted by a Stanford manipulator which trains patients who have suffered an stroke and have lost arm motor skills. The main purpose of MIT-Manus is guiding the human-arm and works in conjunction with a virtual environment for displaying the desired and real reaching exercise carried out by the patient. Another example is the Mirror Image Movement Enabler system, [START_REF] Lum | The mime robotic system for upper-limb neuro-rehabilitation: results from a clinical trial in subacute stroke[END_REF], a PUMA 560based robot, which can impose bilateral 3-D force-position motions. Likewise, the Bimanu-track robot for upper-limbs rehabilitation uses active practise of forearm movements, such as pronation/supination, and wrist flexion/extension into mirror like fashion [START_REF] Schmidt | Upper and lower extremity robotic devices to promote motor recovery after stroke -recent developments[END_REF]. EU Gentle/s Project presents a 3 degree-of-freedom (DoF) haptic device for right-handed subjects, which uses virtual environments for reaching exercises to tackle the arm tracking problem by using a path planning based on smooth polynomials [START_REF] Amirabdollahian | A case study on the effects of a haptic interface on human arm movements with implications for rehabilitation robotics[END_REF]. Finally, ARM-guide [START_REF] Reinkensmeyer | Guidance-based quantification of arm impairment following brain injury: a pilot study[END_REF] drives the forearm along a linear position profile into 2-D space having guided force training in joint space. It is clear that in robotic-mediated-rehabilitation devices either exoskeletons or end-effector devices, the virtual reality technology enhances the performance of upper-limb post-stroke rehabilitation [START_REF]Heart disease & stroke statistics, our guide to current statistics and the supplement to our heart & stroke facts 2009 update[END_REF].

The Rehabilitation Robotics Platform

This section is devoted to present our proposed platform for carrying out the arm therapy by using a right-arm exoskeleton.

The Light-exoskeleton Architecture

The general scheme is presented in the Fig. 1.b). The system is clearly composed by the Light-Exoskeleton and its hardware/software integration; the therapist that handles the therapy session uses a workstation which contains the set of projected scenarios into the screen, following the clinical protocol and giving instructions to the patient during the rehabilitation sessions. The Light-Exoskeleton 2 is a right-arm assistant device, it is composed of five degree-of-freedom (DoF), it is capable of feeding the patient-arm with bio-mechanical force-feedback through the four-active DoF (using four-PWM controlled motors), and the last DoF is used for computing the exerted force direction in the operational space. Ρ ∈ q q q q q q corresponds with the human arm motions, i.e., adduction/abduction, flexion/extension, internal/external rotation, forearm flexion/extension and finally pronation/supination. In addition, one important variable 2 Hereafter Light-Exoskeleton could be called for short L-Exos.

is defined in order to analyse the arm's behaviour, the triplet

3 Ρ ∈ h X
which stands as the temporal space variable of hand-palm; that vector is mapped onto the virtual scenario workspace for the interaction with video-game-like exercises is helping post-stroke (acute mild to moderate) patients handling their arm-stiffness and recovering the neurocontrol of their arm using the constrained kinematics and dynamics; also the brain plasticity through several kinesthetic coordination exercises is treated.

Involved Technologies, Knowledge and their Trends to the Innovation

The performance of the rehabilitation platform depends on the application of several important areas such that innovative concepts and paradigms arise depending on the defined outcomes into arm rehabilitation as well as the patient's conditions. The set of relevant areas are described as follows.

• Applied Robotics. This field is quite important because trough the constrained kinematics and constrained dynamics we can handle the patient's limbs to move either a maximum or minimum angular displacement depending on the clinical protocol; for instance, the weighted-iterative inverse kinematics lets to get working more the internal/external rotation than arm adduction/abduction, but maintaining the angular position set 5 Ρ ∈ q in the Jacobian-null-space, whereas the dynamics of the system is modeled as a Differential-Algebraic-Equation (DAE index-2) yielded by the Lagrange formalism [START_REF] Spong | Robot Modeling and Control[END_REF], representing the motion equations of the exoskeleton in non-free motion, i.e., including the contact of L-Exos with the patient. Likewise, these approaches are attempting in the best case to emulate the occupational therapist in contact with the patient arm; regulating force and position simultaneously or separately depending on the clinical procedure.

• Applied Nonlinear Control. This field is required and innovative into this platform; because of the applications of several nonlinear controllers have demonstrated better performance than linear ones as used on the related work [START_REF] Lugo-Villeda | Regressor-free force/position control of fixed-base exoskeletons for rehabilitation tasks[END_REF]. The whole energy of the system should remain passive (controlled) all time, and these nonlinear controllers gather that condition. Also, the free-model nonlinear controllers are desirable and largely unknown at our best knowledge; it is due to many exoskeletons for rehabilitation have been designed for neuroscience or biomechanical groups, and with only a proportional plus derivative with gravity compensation controllers are used but neglecting all parametric non-modeled patient-induced uncertainties. Therefore, the next set of operation modes have it bases on the nonlinear controllers design:

1. Position Mode. This mode lets the robot to take the control of the upper limbs transferring their position and perhaps their orientation; the patientlimbs are passive.

Triggered-Gains Position Mode.

In this mode, the patient starts applying a force for triggering the motion; the exerted force by the patient modules the percentage of the robot's help, adjusting the gains of the position controller which tracks the imposed trajectory in the task. 3. Composed Force/position Control Mode. The patient is constrained by a defined task in terms of kinesthetic forces and positions onto his/her workspace; it induces active resistance to patient improving in this manner the patient coordination effort. 4. Direct Force Mode. The patients can freely move their arm into a defined space wherein just the exerted force is controlled. 5. Open Loop Mode. This option lets to patient interacting with the virtual environment trough several video-game-like applications; the patients use their actual skills for playing games but exerting forces and positions of their limbs according the game responses. 2 The general closed-loop scheme, the path planning is generated according the virtual reality application into XVR software; afterwards the kinematics and dynamics establish the rehabilitation operation modes trough nonlinear controller described above.

d X d X & ( ) J q + d q d q & , q q & λ q λ Fig.
• Virtual Environments. The virtual scenarios render more interactively the rehabilitation, attracting the full concentration of the patient, at the degree that they feel very comfortable in front of video-game-like applications for carrying out the session. Those scenarios include shapes to be tracked into spatial coordinates within the video-game-like virtual reality application, wherein the therapist can vary parameters like, the target distance, the number of trials, the timing, the maximum force, the depth, see Figure 3.

• Bio-Signals: The analysis of other signals coming from the muscles(EMG) different from the system's states are a good option for measuring the effectiveness and progress of the therapy [START_REF] Lugo-Villeda | Haptic Guidance of Light-Exoskeleton for Arm-Rehabilitation Tasks[END_REF]; even we could know whether the exoskeleton is not well-aligned with the patient-arm rotation axes.

a) b)

Fig. 3 Virtual reality applications with force sensing. a) The reaching video-game-like application, it will be used for our trials. b) Cubes application, it consists into a 3-dimensional puzzle to be organised by the patients.

• Medical Assessment: It includes several scales for assessing a rehabilitation session: a) The Fugl-Meyer scale, b) Modified Ashworth scale, (c) Range of Motion, etc. • Robotic Constrained Therapy (RCT): The main idea of this statement is to constrain from the kinematical and dynamical point of view the right-arm joints, such as the real therapist does; The magnitude of range motion depends on the clinical protocol and it is directly programmed and adjusted into the GUI (graphical user interface) of the therapist's workstation as long as the patient is able to perform an exercise.

Preliminary Experimental Results

Some preliminary experiments were carried out by a healthy people; nevertheless, similar results are expected on real disable people. We have chosen the presented task of the Fig. 3.a) which is one of the most used tasks into rehabilitation, the reaching task. It consists into tracking from initial point to the final point and vice versa a spatial target in a determined lapse; in this experiment we used the first kind of operation mode3 a virtual scenario and the minimum-jerk-criterion for the path planning. Firstly, notice that in Fig. 4 the constrained kinematics is working with the same desired target but with different weights of shoulder joints; the main outcome relies on getting adduction/abduction arm motion to be minimum when varying its weight 100; w 50, w 1, w ,

1 1 1 = = =
but the arm requires more effort for reaching the target on the external/internal rotation; thus constraining the first joint motion. Secondly, Fig. 5 shows the reaching task into spatial coordinates in terms of the handpalm position

3 Ρ ∈ h X
for one target, the constraining planes work like big potential fields that return the hand-palm of the patient when it tries to go away from the desired position. Finally for three targets the desired and actual position, the low spatial errors, and the exerted force along the desired trajectories have shown in Fig. 6. Notice the exerted force during reaching task is not more than 5 Newton; we used the same position mode for all experiments.

Fig. 4 The constrained kinematics using the weighted iterative inverse kinematics. Notice that the adduction/abduction is working less as long as its weight is bigger.

Fig. 5 The constrained dynamics is used into the nonlinear controller; we have constrained the hand-palm to move only onto the desired trajectory by introducing two constraining planes.

Discussions

Until now, this contribution remains on the edge of performing the proposed approach on healthy patients and the other control modes deserve more attention; nowadays clinical pilot trials are actively working. The importance of presenting the required tools is a must. It is clear that many drawbacks are still being a challenge, because of the complexity of the L-Exos and the different behaviour of the patient to patient. The performance of getting a successful rehabilitation platform obeys to the correct use of the involved technologies and its associated knowledge; to our best knowledge, the constrained robotics presents the advantages so clear with respect to other works.

Conclusions and Future Work

We are presenting the involved disciplines for having stable right-arm rehabilitation.

We introduce the Light-Exoskeleton which is intended for human arm rehabilitation suitable for post-stroke patients who present problems with normal arm skill motions. Several kinds of operation modes in non free motion make the system robust, accessible and feasible to be used in disable people. The constrained kinematics and dynamics foster the arm therapy to the robotic constrained therapy as innovative concept; thereby the robot represents the physician as long as we draw advantages from the emerging fields. Preliminary experimental results are shown as well as the involved stages about the system. The neuroplasticity can be managed via kinesthetic channel and coordinated motions imposed by the L-Exos, but always under the supervision of the physician or therapist. As near future work, the application on real patients is actually working as an eight-month clinical programme for assisting arm impaired people at Cisanello Hospital, Pisa, Italy. Also different scenarios are being programmed according to patients needs.

Fig. 1

 1 Fig. 1 The right-arm robotics rehabilitation platform. a) Light-exoskeleton architecture. b) Upper-part rehabilitation workspace. The corresponding generalized coordinates are measured by digital encoders such that the vector } , , , , { = 5 4 3 2 1

  . The exerted force by the patient's hand-palm is measured by means of a three-axial 100N force sensor. 1.a). One of the foremost goals of this platform

Fig. 6

 6 Fig.[START_REF] Perry | Design of a 7 degree-of-freedom upper-limb powered exoskeleton[END_REF] Experimental results of the hand-palm of the patient, the Cartesian error and the exerted force along the trajectory during three-reaching targets in arm rehabilitation.

Notice that both exoskeletons belong to fixed-inertial frame exoskeletons.

The remaining operation modes are working with healthy people but not presented here.


Zina Boussaada

Ahmed Remaci
email: a.remaci@estia.fr

Octavian Curea
email: o.curea@estia.fr

Belkahla Olfa
email: olfa.belkahla@isg.rnu.tn

Haritza Driss

Najiba Mrabet Camblong
email: aritza.camblong@ehu.eus

Bellaaj

Olfa Belkahla Driss

Najiba Mrabet Bellaaj
email: najiba_bm@yahoo.fr

Management Approach for Microgrid Operation Using Multi Agent System (MAS) Technique

Keywords: Smart Interface, management, Multi Agent System (MAS, microgrid, operation mode. I

INTRODUCTION

Because of the increase of environmental conscience, renewable energy has invaded electrical grid. As a result the concept of smart grid was created. With the implantation of renewable generators in dispersed locations, the electrical production passed from centralized to a distributed structure. In fact, the grid has been converted into an array of "smaller" grids, which can work in a connected or islanded operation mode. It is the concept of microgrid. This concept increased the complexity of electricity management and control. In fact, when the microgrid works in connected mode, the direct connection with the main grid doesn't ensure the energy optimization for the system. When the microgrid works in islanded mode, the electric interface is not sufficient to stabilize the microgrid operation. Hence the requirement for a software smart management interfaces, which must ensure the microgrid operation in islanded and connected mode, as well as in the moment of switch between these two operation modes. In the literature, the management of the microgrid operation had been performed using different Energy management frameworks [START_REF] Shi | Evaluating microgrid management and control with an implementable energy management system[END_REF][2], but, artificial intelligence techniques have proven their performances in the management of complex systems particularly the systems with behaviors similar to those of microgrids (Network management, intelligent platform management interface, database management, etc.) [START_REF] Grislin | Agents intelligents en interaction hommemachine dans les systèmes d'information[END_REF]. Assuming that the microgrid consists of a set of heterogeneous entities, which interact with each other's in a distributed way, these characteristics match with the MAS working. In fact, the MAS is a software level based on a distributed operation. Therefore, it was used in several microgrid applications, such as spot market mechanism and economic dispatch [START_REF] Wang | Multi-agent co-ordination for the secondary voltage control in power system contingencis[END_REF], smart grid control [START_REF] Phillips | Agent-based control of distributed infrastructure resources[END_REF], demand response [START_REF] Chung | Control methods for multiple distributed generators in a microgrid system[END_REF], service restoration mechanism [START_REF] Mcarthur | Automating power system fault diagnosis through multi-agent system technology[END_REF], and Virtual Power Plant (VPP) control [START_REF] Cristaldi | Multiagent based power systems monitoring platform: a prototype[END_REF].

The disadvantage of the most of previous researches is that, they considered the agent as all the element of the microgrid. Then, they turned the physical aspect of the microgrid into a purely software aspect, based on the communication between agents, in the same computer or in a different computers by a TCP-IP protocols [START_REF] Kumar Nunna | An agent based energy market model for microgrids with distributed energy storage systems[END_REF]. However, it is not the case of real systems. Therefore, in our research, it is assumed that each agent is implemented in a computing system, associated to each microgrid element. The exchange of information between agents is ensured by a communication system. Besides this system, each agent exchanges information with its associated physical microgrid element. The aim of our research is to ensure the microgrid operation using the above described MAS. The proposed approach is to build a smart management system, which must ensure the balance between supply and demand of energy in the two operation modes (connected and islanded). In islanded mode, the MAS predicts the energy to be produced and that to be consumed in real time in order to supply all local loads. However, in connected mode the mission of MAS is to ensure two tasks: the sale and the purchase of electricity to/from the neighbors' microgrids and the main grid using an optimization algorithm.

The paper was organized as follows. In Section 2, the microgrid will be defined. In Section 3, the MAS will be presented by defining Agent and its classification. Then, Section 4 will present the reasons of the MAS choice. Section 5 will focus on the proposed approach and the description of the different scenarios. Finally, Section 6 will conclud the paper.

II. MICROGRIDS

A. Definition and structure of microgrid

With the integration of renewable energy sources in the electrical grid, the electricity production has become intermittent. This situation requires the integration of storage facilities and units of computing and control to supply the demand of consumers in energy. At this level, the electrical grid passed to a smart grid. In order to maintain the consumptionproduction balance in all circumstances, and to enable the final consumer to better control his demand, the production of electricity has become decentralized. For example, a consumer can install his own Photovoltaic panel and wind turbine on the roof of his house. This is the concept of microgrid. The structure of microgid varies from one microgrid to another. But, as shown in Figure 1, three necessary elements must exist to obtain the microgrid: generators (photovoltaic panel, wind turbine, micro turbine), storage systems (battery, electric vehicle) and loads (house load, electric vehicle). The distributed generators are the producers of energy. The distributed storage systems are the components which store the energy and provide it depending on the situation of microgrid. However, the loads are the consumers of energy.

B. Operation modes of microgrid

Microgrids have two operations modes: the connected and the islanded mode. In connected mode, the microgrid is connected to the main grid, which is the superimposed grid. Therefore, in the connected mode the energy balance between supply and demand is ensured through the exchange of energy with the main grid or the microgrids neighbors, which are connected to the main grid. In islanded mode the microgrid is electrically isolated from the main grid. There are no energy exchange between grids. As a result, maintaining the energy balance in islanded mode becomes a challenging task.

III. MULTI AGENT SYSTEMS (MAS)

A Multi Agent System is a system consisting of several interacting agents with each other's and with the elements of their environment.

A. Agent definition

An agent is an autonomous entity (software or hardware), which acts on itself and on its environment. In the Multi Agent context, an agent is able to communicate with the other agents. Its behavior is the result of its observations, its knowledge, and its interactions with the other agents and the entities of its environment.

B. Properties of Agents

In MAS, agents are characterized by several properties, where the most importants are:

 Autonomy: An agent is able to act without the direct intervention of another entity (agent or human). It can also control its actions and its state.  Reactivity: An agent perceives its environment and responds to the changes occurring there.  Communication: An agent can communicate with another agents and entities (Software, Hardware or human).  Sociability: An agent can interact with the other agents in a collaborative or a competitive way in order to achieve its objectives.  Pro-activity: An agent is able, on its own initiative, to set goals to reach its objectives [START_REF] Boussaada | Multi-agent systems for the dependability and safety of microgrids[END_REF].

C. Classification of Agents

Based on the evocated properties, researchers proposed several classifications of agents. According to Grislin et al. [START_REF] Grislin | Agents intelligents en interaction hommemachine dans les systèmes d'information[END_REF], agents can be classified according to the degree of autonomy, of cooperation and adaptation. These properties are generally seen as main characteristics in distributed artificial intelligence [START_REF] Adam | Modèle d'organisation multi-agent pour l'aide au travail coopératif dans les processus d'entreprise : application aux systèmes administratifs complexes[END_REF]. Three types of agents were distinguished in the MAS [START_REF] Chaib-Draa | Interaction between agents in routine, familiar and unfamiliar situations[END_REF]:  Reactive agent: This agent has limited ability for communication, has little or not a model on itself, on other agents or on the environment. Its behavior is of type "stimulus-response", then it operates based on receiving messages.  Cognitive agent: This agent has a more "thoughtful" behavior, resulting from a choice among a set of possible actions. This choice is a result of reasoning.  Hybrid agent: The hybrid agent is neither reactive nor cognitive. In fact, it has some level of every agent property. Therefore hybrid agent behavior can be placed between reactive agent and cognitive agent.

IV. MAS IN MICROGRIDS

Based on their size, their heterogeneity and their evolving nature, microgrids are considered complex systems. The choice of MAS technique for the microgrid control is justified by several reasons. In fact, microgrids are characterized by their distributed nature and the interaction of heterogeneous elements between each other's and with their environment. Microgrid elements have only a local view of the grid and do not have access to the global behavior of the system. Only the operators of the grid have a global view of the system macroscopic behavior. In addition, the microgrid has a history; its size and its structure vary over time, so its past is at least partially responsible for its current situation. Besides, in connected mode, the microgrid accomplishes interactions based on the market mechanism, which is one of the basic tasks of MAS. In fact, to supply local load, the microgrid performs a negotiation phase with main grid and neighbors' microgrids to purchase energy. In the same way, to supply external load, the microgrid negotiates to sell its production of energy.

V. PROPOSED APPROACH

Several propositions were performed to control microgrids using MAS [3]-[10] [START_REF] Curea | Multi-Agent Systems for the Dependability of Microgrids[END_REF]. The main principle is to develop a representative agent for each element of the microgrid, and one additional agent, which controls and represents the microgrid in its external environment (Main grid and neighbors' microgrids). The proposed approach of this paper is based on the capacity of information exchange between each agent and its associated microgrid element. This exchange is bidirectional. In fact, the agent is able to perform two functions: it recovers measures (current, voltage, temperature, etc.) and it acts on the physical part of the microgrid, in order to perform different operations as connection/de-connection, and change of operation point. Thus, the MAS presents the smart interface of the microgrid, when each agent is embedded in a system set up with the associated microgrid element.

As described in Figure 2, our first microgrid prototype consists of photovoltaic (PV) panels, batteries, and loads. The main grid is represented by a "global supervisor agent" and the microgrid by a "local supervisor agent". Each element of the microgrid is represented by a local agent: load agent, battery agent and PV agent. "Interface agent" was added in order to detect the connection and disconnection of microgrid elements and to ensure the interaction with the user platform. The interaction between agents is presented in Figure 3. The principle of the proposed management approach is based on sending and receiving of messages between agents in order to exchange information and to take decisions. Local agents communicate with their local supervisor agent and if the operation mode is connected, the local supervisor agent communicates with the global supervisor and the local supervisor agents of the neighbors' microgrids.

The proposed algorithm for the operation management is presented in Figure 4. It is important to note that drawn diagram describes both the progress of management program and the interaction between agents.

The used variables in the following paragraph and Figure 4 are as follows:

Pc: The power quantity to be consumed. Pp: The power quantity to be produced. Pbat: The available power of the battery. SOC: The battery state of charge. It is supposed that in the starting state the demand and the supply are balanced. The MAS must be able to manage events, such as power demand in Figure 4. As a result of this event, three cases are possible:

1/ If the amount of produced power Pp is greater than or equal to the requested power Pc, loads are supplied and the local supervisor agent proposes to supply battery by Pp-Pc. If the SOC is less than 100% the battery is supplied. And after this, if there is an excess of power and the operation mode is connected, the local supervisor agent initiates a communication phase with the global supervisor agent and the neighbors' supervisor agents in order to perform a power sales phase.

2/ If the produced power summed with the available battery power is greater than or equal to the requested power Pc, loads are supplied by PV and battery.

3/ If the produced power summed with the available battery power is not enough to supply all loads, the management of this event depends on the operation mode. In connected mode, the local supervisor agent starts a power purchase phase with the global supervisor agent and the neighbors' supervisor agents, and it takes decisions based on an optimization algorithm identifying the minimal cost of the energy. Therefore, in islanded mode, the microgrid is no more able to supply all loads, so some loads can be disconnected. As a result, the local supervisor agent must distribute a priority order to all loads. The loads with the highest priority order are the critic loads. They must be supplied before other loads.

VI. CONCLUSION AND FUTURE WORK

This paper proposed a management approach of microgrid using MAS technique. Based on the distributed nature of microgrid and the heterogeneity of its elements, the MAS presents an adequate smart management interface for the microgrid. This interface had the mission of ensuring energy balance between demand and supply in the connected and the islanded mode. The next step of this work is the implementation of agents in order to validate results.

Figure 1 .

 1 Figure 1. Example of Microgrid structure

Figure 2 .Figure 3 .

 23 Figure 2. MAS for the proposed Microgrid management approach

Figure 4 .

 4 Figure 4. Proposed management algorithm for microgrid operation