R. Belkacemi, A. Babalola, F. Ariyo, IEEE Members

Center for Energy System Research

Tennessee Technological University

Cookeville, TN, USA

rbelkacemi@tntech.edu

Abstract −−−− In this Work, we investigate the use of two-way

communication to perform distributed restoration of smart power

grid distribution systems. The concept used in this research is

based on the distributed and intelligent multi-agent system

technology where multiple smart entities are geographically

spread and if equipped with communication capability these

entities are able to reach goals or solutions that would have been

impossible to reach with a single or a local control. The

technology is implemented on the West Virginia Super Circuit to

validate the theory. The results show that proposed system can

restore the power in a timely manner without violating any

constraints.

Index terms — Restoration, Agents, Distribution, Outages,

Smart Grid.

I. INTRODUCTION AND MOTIVATION

he proposed distributed intelligent control architecture is a

concept that aims at transforming the way energy

management of electric power systems is performed.

Distributed intelligence (Agents) is designed to anticipate and

mitigate stress in power systems, and autonomously manage,

efficiently and economically, the energy in a large electric

power system with intermittent generation sources and

controllable load serving entities.

The two-way communication platform concept of smart power

grids made it possible to integrate distributed and intelligent

systems such as Multi-Agent technology to manage the

transfer of energy at a global level. The distributed nature of

the power grid through a vast geographic area makes this

technology the most suitable approach to automatically

manage, operate, and control the grid in real time.

Distributed Intelligent Agents are adaptive, trained, self-aware,

self-healing, and autonomous control systems that respond

rapidly at the local level, to unburden centralized control

systems and human operators, and often are capable of

reaching goals difficult to achieve by an individual system.

This technology is expected to be the basis for appropriate

corrective actions to eliminate, mitigate, and/or prevent

outages and blackouts, thus improve grid reliability.

The objective of this research is to design an adaptive and

distributed multi-level control architecture based on the

Human Immune System Network acting as a multi-agent

system. The system will be able to manage the flow of the

power by acting on generators, renewables, and

Ali Feliachi, IEEE Senior Member

Advanced Power and Electricity Research Center

West Virginia University

Morgantown, WV, USA

afeliachi@wvu.edu

transmission/distribution systems in a distributed fashion using

the two-way communication enabled by the smart grid

technology in order to relieve power lines, improve stability

response in case of disturbances and allow large scale

penetration of renewables.

In the literature, the immune system theory is looked at as

centralized algorithm used for optimization [1][2]. This

approach localizes the algorithm at a single cell type level and

does look at the system as whole with the different type of

cells and biological communication that takes place. This type

of approaches is not suitable for smart grid applications

because of the tendency for more distributed control to take

advantage of the two-way communication infrastructure

offered by the modern grids. In [3-6], the authors try to

implement a Multi agents system to perform restoration. The

authors fail to incorporate any intelligence into the agent’s

architecture and rely solely on the communication. This

approach fails in real system because the losses and will

violate the voltage constraint as shown in this work.

Furthermore, almost all the approaches are implemented on

radial systems and will fail to work in a meshed or two way

power flow systems while a high penetration of the of

renewables is expected in smart grid systems.

In this work, a distributed, intelligent, and adaptive agent

based system is developed by designing a network with all the

immune system components, Meaning as a distributed system

with its different type of cells, namely Helper-cell, T-cell, B-

cell etc., including their behaviors and the communication that

takes place between theses cells to build a complete, intelligent

Multi Agent System for Smart Grid Control.

II. DISTRIBUTED ARCHITECTURE DESIGN

Distributed intelligent agents are computational components

distributed throughout the power grid that can sense the states

of the system such as voltages and currents and act on

actuators to alter the operation of the system in case of stress

or disturbance on the grid.

A centralized control of vast power systems with DGs, smart

meters would be very complex and data has to flow to a single

point which in turn increases the probability for failure.

In this work, a Zigbee network is developed to allow

Restoration of Smart Grid Distribution System

using Two-Way Communication Capability

T

978-1-4799-1255-1/13/$31.00 ©2013 IEEE

communication between the nodes or agents, as shown in

Fig.2. The FIPA (Foundation for Intelligent Physical Agents)

standard is used as protocol for communication.

Fig. 2. Communication Network

III. DISTRIBUTED IMMUNE SYSTEM NETWORK

An adaptive and intelligent immune system network is

designed by observing the different immune cells of the body.

The network of cells acts exactly as a distributed entities with

a pre-programmed tasks. These cells communicate and work

together as a team to attack viruses and bacteria invading the

body. Fig.3 illustrates the process followed by the cells to

eliminate threats to the body.

Fig. 3. Immune Network Behavior

In the first phase of the attack the Macrophage consumes the

Virus or Bacteria and binds to a helper T-cell (TH) for antigen

presentation. The Helper T-cell causes other helper T-cells,

killer-T cells, B-cells to divide and mount the attack from

different angles.

To apply this theory in Smart grid systems, a mapping has to

be done. The table below illustrates how the mapping is

performed.

TABLE I

IMMUNE SYSTEM POWER SYSTEM MAS ANALOGY

IV. DEPLOYMENT ON THE TEST SYSTEM

In this section the developed network is implemented on

Feeder #3 and Feeder#4 of the test system, shown in Fig.4.

Fig.4. Test Circuit diagram

A zonal architecture is deployed on the system to match the

requirements of the circuit and the placement of the tie

switches on the feeders as illustrated in Fig.5.

Fig. 5. Zoning of the feeder line

A three phase permanent fault is applied in zone#6 (see Fig.

4). The following figures show voltages and currents during

the scenario and the process of restoration taken by the

developed architecture.

Fig. 6 depicts the waveform of the currents and voltages seen

at the substation. It can be observed that the protection system

,which a substation recloser, kick in first trying to clear the

fault. Since the fault is permanent, the recloser stay open after

two attempts. a signal is sent from the agent residing at the

substation level to the DG Agent in order to trip the DG to

avoid any damage as shown in Fig. 9. The fault isolation phase

follows right after that as illustrated in Fig. 7.

Fig. 6. Current and Voltage at the substation

Fig. 7. Current and Voltage in the faulted zone#6

Then, the restoration the rest of the system starts as shown in

Fig. 8. The DG is then reconnected by the intelligent network

as depicted in Fig. 9.

Fig. 8. Current and Voltage in zone#7

Fig. 9. Current and Voltage in zone#8

In Fig.11 we are depicting the two-way communication that

took place between these intelligent entities during the entire

scenario.

In the following, the agent network proposed in the literature

[3][4][6] is implemented on the same system. The same

scenario is performed without prior training of the system to

compare it to Multi Agent System proposed in this research.

We can observe that since no overloading of the system is

expected, the agents in this case, restore both zones#7&8 from

the neighboring feeder W#3. Because no training for voltage

violations was offered to the Agent at the design level this

strategy resulted in voltage violation or voltage drop below the

limit in zone#8 as shown in Fig. 10.

Fig. 10. Voltage in Zone#8

Fig. 11. Inter Agent Communication

V. CONCLUSION

The focus of this work is the development and deployment

of software Multi Agent System for Smart Grid power

distribution management. The paper addresses the use of the

Human Immune System viewed as a Multi-Agent System to

perform self-healing and control of the grid by automatic fault

location and isolation, reconfiguration and restoration. The

Simulation results show that the technology is very promising

and effective. The detailed model of immune system based

multi-agent system described above will be implemented in

hardware platform to validate the results.

VI. REFERENCES

[1] A. Ahuja, S. Das, and Pahwa, “An AIS-ACO Hybrid Approach for

Multi-Objective Distribution System Reconfiguration”, IEEE

transactions on power systems, vol. 22, no.3, August 2007, pp. 1101-

1111 [2] R. Belkacemi, A. Feliachi, "An Immune System Approach for Power

System Automation and Self Healing" IEEE PSCE09, March 15-18,

2009, Seattle, WA.

[3] J. M. Solanki, S. Khushalani and N. N. Schulz, “A Multi-Agent

Solution to Distribution Systems Restoration,” IEEE Transactions on

Power Systems, Vol. 22, No. 3, pp 1026 – 1034, August 2007 [4] J. G. Gomez-Gualdron, and M. Velez-Reyes “Self-Reconfigurable

Electric Power Distribution System using Multi-Agent Systems,”

Electric Ship Technologies Symposium, pp 180 – 187, May 2007

[5] T. Nagata and H. Sasaki, “A Multi-agent Approach to Power System

Restoration,” IEEE Transactions on Power Systems, Vol. 17, No. 2, pp

457 – 462, May 2002 [6] K. Nareshkumar, M.A. Choudhry, and H.J. Lai, A. Feliachi,

“Application of multi-agents for fault detection and reconfiguration of

power distribution systems,” Proceedings of the 2009 Power & Energy

Society General Meeting.

[7] S. Chouhan, H. Wan, H.J. Lai, A. Feliachi, and M.A Choudhry,

“Intelligent reconfiguration of smart distribution network using multi-

agent technology ,” Proceedings of the 2009 Power & Energy Society

General Meeting.

[8] H. Inan, “West Virginia Super Circuit Project_Preliminary Design

Document”, Version 2.0, October 11, 2010

[9] G. Weiss, “Multiagent Systems: A Modern Approach to Distributed

Artificial Intelligence,” The MIT Press, 2000