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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.


A complete model of MACSimJX.
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fig9: A complete model of MACSimJX.

Mentions: Initially the environment variables of solar microgrid are sensed by the sensors installed in all the physical devices of the solar microgrid. The complete model of MACSimJX is shown in Figure 9, which shows how the signals are given from MACSim client to the MACSim server. Once simulation signals arrive at the MACSim server, they are passed on to the agent model, which is divided into two parts, agent environment and agent task force (ATF). The agent environment provides essential facilities such as coordination and messaging. The agent environment has an agent coordinator and an agent server. The agent coordinator posts messages to ATF which assigns the work to various agents, requesting them to carry out any operations necessary to prepare outputs for the specified time step. The ATF consists of all the agents that jointly operate on the data arriving from Simulink in order to accomplish the goal of optimizing the energy management of solar microgrid. The decentralized approach of MAS with the runtime efficiency of JADE improves the performance and reduces the computational complexity. Then, the computed outputs are passed back to the agent environment. Once the agent environment has the completion messages from all agents, the output values are passed to the pipe server to be returned to Simulink. The messaging, therefore, provides synchronization with Simulink's simulation cycle so that the messages can be sent as command signals to the actuators for switching operation of microgrid devices.


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

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

A complete model of MACSimJX.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig9: A complete model of MACSimJX.
Mentions: Initially the environment variables of solar microgrid are sensed by the sensors installed in all the physical devices of the solar microgrid. The complete model of MACSimJX is shown in Figure 9, which shows how the signals are given from MACSim client to the MACSim server. Once simulation signals arrive at the MACSim server, they are passed on to the agent model, which is divided into two parts, agent environment and agent task force (ATF). The agent environment provides essential facilities such as coordination and messaging. The agent environment has an agent coordinator and an agent server. The agent coordinator posts messages to ATF which assigns the work to various agents, requesting them to carry out any operations necessary to prepare outputs for the specified time step. The ATF consists of all the agents that jointly operate on the data arriving from Simulink in order to accomplish the goal of optimizing the energy management of solar microgrid. The decentralized approach of MAS with the runtime efficiency of JADE improves the performance and reduces the computational complexity. Then, the computed outputs are passed back to the agent environment. Once the agent environment has the completion messages from all agents, the output values are passed to the pipe server to be returned to Simulink. The messaging, therefore, provides synchronization with Simulink's simulation cycle so that the messages can be sent as command signals to the actuators for switching operation of microgrid devices.

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.