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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.


Flowchart.
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Related In: Results  -  Collection


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fig6: Flowchart.

Mentions: The solar power, load, state of charge (SOC) of the battery, noncritical loads, and dynamic pricing of grid are monitored hourly, and based on these data, the agent takes best possible actions autonomously for dynamic energy management of the solar microgrid in a distributed environment. Considering all the possible options available for the solar microgrid, a flow chart is drawn as shown in Figure 6. The proposed system has the following agents: solar power generator agent, load agent, grid agent, diesel agent, and control agent. Each PV system has all these agents. Multiagent programming is done in JADE in Eclipse environment. The overall procedure is the following.


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

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

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

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

fig6: Flowchart.
Mentions: The solar power, load, state of charge (SOC) of the battery, noncritical loads, and dynamic pricing of grid are monitored hourly, and based on these data, the agent takes best possible actions autonomously for dynamic energy management of the solar microgrid in a distributed environment. Considering all the possible options available for the solar microgrid, a flow chart is drawn as shown in Figure 6. The proposed system has the following agents: solar power generator agent, load agent, grid agent, diesel agent, and control agent. Each PV system has all these agents. Multiagent programming is done in JADE in Eclipse environment. The overall procedure is the following.

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.