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Multiagent Systems Based Modeling and Implementation of Dynamic Energy Management of Smart Microgrid Using MACSimJX
Leo Raju1, R S Milton1, Senthilkumaran Mahadevan1
1SSN College of Engineering, Chennai, Tamil Nadu 603110, India.
Thescientificworldjournal
|April 30, 2016
Summary
This study implements a multiagent system (MAS) for efficient solar microgrid energy management. The system optimizes power exchange and enhances operational efficiency through a decentralized approach.
Area of Science:
- Electrical Engineering
- Computer Science
- Artificial Intelligence
Background:
- Solar microgrids require advanced energy management due to intermittent power generation and dynamic load conditions.
- Existing control systems often struggle with the complexity of decentralized energy resources and real-time adjustments.
Purpose of the Study:
- To implement a multiagent system (MAS) for advanced distributed energy management and demand-side management in a solar microgrid.
- To enhance the operational efficiency and optimize power exchange within the solar microgrid.
Main Methods:
- Utilized the Java Agent Development Environment (JADE) framework for MAS implementation.
- Integrated JADE with MATLAB/Simulink using Multiagent Control Using Simulink with Jade Extension (MACSimJX) for microgrid component control.
- Developed a simulation environment to emulate distributed microgrid operations and evaluate agent performance.
Main Results:
- The MAS in JADE maximized solar microgrid operational efficiency through a decentralized approach.
- Runtime efficiency was increased due to the JADE framework.
- Autonomous demand-side management optimized power exchange considering solar intermittency, load randomness, and grid price variations.
Conclusions:
- The implemented MAS effectively manages solar microgrids, improving efficiency and enabling autonomous demand-side management.
- The JADE-MATLAB integration via MACSimJX provides a robust platform for controlling complex microgrid dynamics.
- This approach offers a promising solution for optimizing renewable energy integration and grid stability.
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