Limits...
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.


Simulink model of solar microgrid.
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4834404&req=5

fig10: Simulink model of solar microgrid.

Mentions: This paper aims at dynamic energy management by distributed automation of solar microgrid in a distributed environment. The important components of solar microgrid are programmed in agents so the component status of MATLAB/Simulink can be sent to the JADE agent system. If the breaker needs to switch after judgment, received after operation of agents, the JADE agent system will send a signal to the corresponding breaker in MATLAB/Simulink, which in turn is given to the physical breaker for real-time action. The approach considers a two-layer framework comprising agent layer containing the agent based control and functional layer, where the physical infrastructure is simulated in MATLAB/Simulink. The behavior parts of the agents (i.e., decisions and control strategies) and coordinating functionalities are done in the agent layer aiming to optimize microgrid operation. The functional layer includes the model of the real solar microgrid comprising solar units load, batteries, diesel unit, and grid. MACSimJX acts as a middleware linking these two. The functional layer gets the values of the environmental variables and passes them to the agent layer. The agent layer observes values and coordinates with required agents using the decentralized approach of MAS operating in JADE and takes necessary actions for optimizing the solar microgrid under dynamic environment. The agent layer needs to interact with the function layer to have a mean to test the control strategies. The agent layer consists of department load (LDA) and solar (SDA) and battery agents (BDA) and hostel load (LHA) and solar (SHA) and battery agents (BHA) apart from grid (GA) and diesel agents (DA). The control agent (CA) monitors and controls all the agents. The agent layer uses MATLAB/Simulink as gateways to translate semantics from agent world to services world, where the commands can be physically executed. The quality of service and the dynamic energy management are done by proper control and management strategies, which accommodate different heterogeneous entities and also remain secure, sustainable, and reliable. The MATLAB/Simulink model of the solar microgrid is shown in Figure 10.


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

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

Simulink model of solar microgrid.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig10: Simulink model of solar microgrid.
Mentions: This paper aims at dynamic energy management by distributed automation of solar microgrid in a distributed environment. The important components of solar microgrid are programmed in agents so the component status of MATLAB/Simulink can be sent to the JADE agent system. If the breaker needs to switch after judgment, received after operation of agents, the JADE agent system will send a signal to the corresponding breaker in MATLAB/Simulink, which in turn is given to the physical breaker for real-time action. The approach considers a two-layer framework comprising agent layer containing the agent based control and functional layer, where the physical infrastructure is simulated in MATLAB/Simulink. The behavior parts of the agents (i.e., decisions and control strategies) and coordinating functionalities are done in the agent layer aiming to optimize microgrid operation. The functional layer includes the model of the real solar microgrid comprising solar units load, batteries, diesel unit, and grid. MACSimJX acts as a middleware linking these two. The functional layer gets the values of the environmental variables and passes them to the agent layer. The agent layer observes values and coordinates with required agents using the decentralized approach of MAS operating in JADE and takes necessary actions for optimizing the solar microgrid under dynamic environment. The agent layer needs to interact with the function layer to have a mean to test the control strategies. The agent layer consists of department load (LDA) and solar (SDA) and battery agents (BDA) and hostel load (LHA) and solar (SHA) and battery agents (BHA) apart from grid (GA) and diesel agents (DA). The control agent (CA) monitors and controls all the agents. The agent layer uses MATLAB/Simulink as gateways to translate semantics from agent world to services world, where the commands can be physically executed. The quality of service and the dynamic energy management are done by proper control and management strategies, which accommodate different heterogeneous entities and also remain secure, sustainable, and reliable. The MATLAB/Simulink model of the solar microgrid is shown in Figure 10.

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.