Integrated Simulation Environment for Investigation of Multi-Agent Systems in Smart Grids Applications

2019 ◽  
Author(s):  
Manswet Banka ◽  
Krzysztof Rudion
Keyword(s):  
Smart Grids ◽  
Simulation Environment ◽  
Multi Agent Systems ◽  
Integrated Simulation ◽  
Agent Systems ◽  
Multi Agent

Applications of Multi-Agent Systems in Smart Grids: A survey

2014 ◽  
Author(s):  
Ghezlane Halhoul Merabet ◽  
Mohammed Essaaidi ◽  
Hanaa Talei ◽  
Mohamed Riduan Abid ◽  
Nacer Khalil ◽  
...  
Keyword(s):  
Smart Grids ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

The Agent Visualization System: A Graphical and Textual Representation for Multi-Agent Systems

2005 ◽  
Vol 4 (2) ◽  
pp. 83-94 ◽  
Author(s):  
Hala Mostafa ◽  
Reem Bahgat
Keyword(s):  
Information Visualization ◽  
Simulation Environment ◽  
Multi Agent Systems ◽  
Agent Based Simulation ◽  
Time Step ◽  
Visualization System ◽  
Agent Based ◽  
Agent Systems ◽  
Simulation Scenario ◽  
Multi Agent

As scientists from various domains increasingly resort to agent-based simulation for a more thorough understanding of real-world phenomena, the need for a simulation environment that facilitates rapid development of multi-agent systems is growing. Such a platform should provide means of visualizing the simulated scenario. In this paper we present the agent visualization system, the first system of its kind to specifically focus on catering to the visualization needs of agent-based simulation. The proposed system is a generic add-on that equips a simulation environment with a rich set of visualization facilities offering a variety of textual and graphical browsers that allow the modeler to detect trends and relationships in the simulation scenario. Some techniques from the field of information visualization were adapted and added to the system, while others were devised especially to be used in it. Regardless of their origin, all visualization techniques were thoroughly revised to make them generic enough to fit in our generic system. Agent visualization is more challenging than traditional information visualization in more than one respect. One of them is that the data to be visualized is not static; the simulation system is constantly producing data with every time step. Moreover, the sheer amount of data, together with its diversity, call for special adaptations to ensure that the system remains responsive and generic. To illustrate the various features of the proposed agent visualization system, we present a visualization of MicroTerra; a simulation scenario involving a group of beings trying to maximize their food intake.

scholarly journals Distributed Schemes, Innovative Solutions for Smart Grids: P2P, Multi-Agent Systems & Blockchain

Proceedings ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 5
Author(s):  
Tatiana Loureiro ◽  
Juan Espeche ◽  
Meritxell Vinyals ◽  
Ugo Stecchi ◽  
Dimitrios Tzovaras ◽  
...  
Keyword(s):  
Smart Grids ◽  
Discussion Forum ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Innovative Solutions ◽  
Multi Agent ◽  
Common Goals ◽  
And Storage

Clustering workshop of low TRL Smart Grids and Storage projects, focused on distributed schemes such as MAS, blockchain, P2P, among others. This workshop was organized by DRIvE project. The objective of the event was to establish common goals and methodologies among the participants, and to have a relevant discussion forum.

Swarm and Multi-agent Time-based A* Path Planning for Lighter-Than-Air Systems

2020 ◽  
Vol 08 (03) ◽  
pp. 253-260
Author(s):  
Jason Gibson ◽  
Tristan Schuler ◽  
Loy McGuire ◽  
Daniel M. Lofaro ◽  
Donald Sofge
Keyword(s):  
Path Planning ◽  
Dynamic Simulation ◽  
Real World ◽  
Simulation Environment ◽  
Multi Agent Systems ◽  
Time Step ◽  
External Disturbances ◽  
Agent Systems ◽  
Multi Agent ◽  
Velocity Information

This work develops and implements a multi-agent time-based path-planning method using A*. The purpose of this work is to create methods in which multi-agent systems can coordinate actions and complete them at the same time. We utilized A* with constraints defined by a dynamic model of each agent. The model for each agent is updated during each time step and the resulting control is determined. This results in a translational path that each of the agents is physically capable of completing in synchrony. The resulting path is given to the agents as a sequence of waypoints. Periodic updates of the path are calculated, utilizing real-world position and velocity information, as the agents complete the task to account for external disturbances. Our methodology is tested in a dynamic simulation environment as well as on real-world lighter-than-air robotic agents.

Fault Location Technique Using Distributed Multi Agent-Systems in Smart Grids

2021 ◽  
pp. 607-613
Author(s):  
Mohamed Azeroual ◽  
Younes Boujoudar ◽  
Tijani Lamhamdi ◽  
Hassan EL Moussaoui ◽  
Hassane EL Markhi
Keyword(s):  
Smart Grids ◽  
Fault Location ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

scholarly journals Mosaik and PADE: Multiagents and Co-simulation for smart grids modeling

2020 ◽  
Vol 27 (2) ◽  
pp. 107-115
Author(s):  
Lucas Silveira Melo ◽  
Filipe Saraiva ◽  
Ruth Leão ◽  
Raimundo Furtado Sampaio ◽  
Giovanni Cordeiro Barroso
Keyword(s):  
Distributed Generation ◽  
Smart Grids ◽  
Low Voltage ◽  
Electricity Distribution ◽  
Multi Agent Systems ◽  
Distribution Grid ◽  
Distributed Generations ◽  
Agent Systems ◽  
Multi Agent ◽  
Study Case

This paper describes the integration process between two tools in order to perform co-simulation for representation and analysis of dynamic environments in the context of smart grids. The integrated tools are Mosaik, a software to co-simulation management, and PADE, a software to multi-agent systems development. As a study case for demonstrate the integration, a scenary was utilized composed of a low voltage electricity distribution grid with 37 load bus, 20 photo-voltaic distributed generations, randomly connected to load bus, as well as, 20 PADE agents associated to distributed generation, modeling the behavior of electricity storage systems. The simulation results show the integration happening and demonstrate how useful is to model the dynamics of distributed electric resources with multi-agent systems.

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