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Multiagent Systems Based Modeling and Implementation of Dynamic Energy Management of Smart Microgrid Using MACSimJX.

Raju L, Milton RS, Mahadevan S - ScientificWorldJournal (2016)

Bottom Line: The microgrid environment variables are captured through sensors and given to agents through MATLAB/Simulink and after the agent operations in JADE, the results are given to the actuators through MATLAB for the implementation of dynamic operation in solar microgrid.Autonomous demand side management is implemented for optimizing the power exchange between main grid and microgrid with intermittent nature of solar power, randomness of load, and variation of noncritical load and grid price.These dynamics are considered for every time step and complex environment simulation is designed to emulate the distributed microgrid operations and evaluate the impact of agent operations.

View Article: PubMed Central - PubMed

Affiliation: SSN College of Engineering, Chennai, Tamil Nadu 603110, India.

ABSTRACT
The objective of this paper is implementation of multiagent system (MAS) for the advanced distributed energy management and demand side management of a solar microgrid. Initially, Java agent development environment (JADE) frame work is used to implement MAS based dynamic energy management of solar microgrid. Due to unstable nature of MATLAB, when dealing with multithreading environment, MAS operating in JADE is linked with the MATLAB using a middle ware called Multiagent Control Using Simulink with Jade Extension (MACSimJX). MACSimJX allows the solar microgrid components designed with MATLAB to be controlled by the corresponding agents of MAS. The microgrid environment variables are captured through sensors and given to agents through MATLAB/Simulink and after the agent operations in JADE, the results are given to the actuators through MATLAB for the implementation of dynamic operation in solar microgrid. MAS operating in JADE maximizes operational efficiency of solar microgrid by decentralized approach and increase in runtime efficiency due to JADE. Autonomous demand side management is implemented for optimizing the power exchange between main grid and microgrid with intermittent nature of solar power, randomness of load, and variation of noncritical load and grid price. These dynamics are considered for every time step and complex environment simulation is designed to emulate the distributed microgrid operations and evaluate the impact of agent operations.

No MeSH data available.


Solar microgrid.
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Related In: Results  -  Collection


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fig1: Solar microgrid.

Mentions: The architecture of the considered solar microgrid is shown in Figure 1. The consumer can cover his demand partly by using the electricity produced by the local renewable (solar) generator, partly by stored electricity in the battery when the solar source is not available. The battery is charged when the solar source is available, and can discharge the storage when needed. The consumer has the possibility to control the storage and the solar power generator. The challenge in solar microgrid is that the power supply is intermittent in nature and with the ultracapacitors and battery the randomness in the supply should be managed so that the load is given constant supply all the time irrespective of the nature of the solar power. The battery can be charged by the solar power or from the grid.


Multiagent Systems Based Modeling and Implementation of Dynamic Energy Management of Smart Microgrid Using MACSimJX.

Raju L, Milton RS, Mahadevan S - ScientificWorldJournal (2016)

Solar microgrid.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4834404&req=5

fig1: Solar microgrid.
Mentions: The architecture of the considered solar microgrid is shown in Figure 1. The consumer can cover his demand partly by using the electricity produced by the local renewable (solar) generator, partly by stored electricity in the battery when the solar source is not available. The battery is charged when the solar source is available, and can discharge the storage when needed. The consumer has the possibility to control the storage and the solar power generator. The challenge in solar microgrid is that the power supply is intermittent in nature and with the ultracapacitors and battery the randomness in the supply should be managed so that the load is given constant supply all the time irrespective of the nature of the solar power. The battery can be charged by the solar power or from the grid.

Bottom Line: The microgrid environment variables are captured through sensors and given to agents through MATLAB/Simulink and after the agent operations in JADE, the results are given to the actuators through MATLAB for the implementation of dynamic operation in solar microgrid.Autonomous demand side management is implemented for optimizing the power exchange between main grid and microgrid with intermittent nature of solar power, randomness of load, and variation of noncritical load and grid price.These dynamics are considered for every time step and complex environment simulation is designed to emulate the distributed microgrid operations and evaluate the impact of agent operations.

View Article: PubMed Central - PubMed

Affiliation: SSN College of Engineering, Chennai, Tamil Nadu 603110, India.

ABSTRACT
The objective of this paper is implementation of multiagent system (MAS) for the advanced distributed energy management and demand side management of a solar microgrid. Initially, Java agent development environment (JADE) frame work is used to implement MAS based dynamic energy management of solar microgrid. Due to unstable nature of MATLAB, when dealing with multithreading environment, MAS operating in JADE is linked with the MATLAB using a middle ware called Multiagent Control Using Simulink with Jade Extension (MACSimJX). MACSimJX allows the solar microgrid components designed with MATLAB to be controlled by the corresponding agents of MAS. The microgrid environment variables are captured through sensors and given to agents through MATLAB/Simulink and after the agent operations in JADE, the results are given to the actuators through MATLAB for the implementation of dynamic operation in solar microgrid. MAS operating in JADE maximizes operational efficiency of solar microgrid by decentralized approach and increase in runtime efficiency due to JADE. Autonomous demand side management is implemented for optimizing the power exchange between main grid and microgrid with intermittent nature of solar power, randomness of load, and variation of noncritical load and grid price. These dynamics are considered for every time step and complex environment simulation is designed to emulate the distributed microgrid operations and evaluate the impact of agent operations.

No MeSH data available.