Department of Computer and Information Technology (CIT) Byung-Cheol Min 1,2, Hina Chaudhry 1,2, Eric T. Matson 1,2, J. Eric Dietz 1,3, Anthony Smith 1,2

Department of Computer and Information Technology (CIT)

Byung-Cheol Min1,2, Hina Chaudhry1,2, Eric T. Matson1,2, J. Eric Dietz1,3, Anthony Smith1,2

Computer and Information Technology1, M2M Lab2, Purdue Homeland Security Institute3

Purdue University, West Lafayette, IN, USA

http://www.purdue.edu/m2m [email protected]

Rural Energy Security Using Autonomous Micro-turbine Smart Grids

IEEE Rural Electric Power Conference 10-13 April 2011

Chattanooga, TN, USA


Presentation Outline

• Motivation Independent Power Generation System Abundant Power

• Method Design of a Cooperative, Autonomous

Multiagent Micro-turbine Smart Grid Fuzzy Logic

• Simulation• Conclusion• Current & Future Works


What is Smart Grid?

(http://venturebeat.com/2011/02/01/how-secure-is-the-smart-grid/)


Motivation

(b) Cooperative agents for management of wind power system

• Control• Balance• ManageThe whole power system

(a) House wind turbine

Agent1 Agent2

Agent3

Agent4Agent5

Agent6

(http://good-energy.typepad.com)

The aim of this project is to develop a cost-effective1, scalable2, portable3 , and plug-and-play4 wind power system and design cooperative agents for its management.

+ +

+ +⋯


Wind Power System

Agent P1ڮ

House Representative

Agent H1

ڮ

Agent Pi Agent P1

Agent P2

Agent P3Agent P4

Agent P5

Agent Hj Agent H1

Agent H2

Agent H3Agent H4

Agent H5

Wind Power System

Agent P2

Wind Power System

Agent Pi

Wind Power System

Agent P1ڮ


Agent H2

Wind Power System

Agent P2

Wind Power System

Agent Pi

Wind Power System

Agent P1ڮ


Agent Hj

Wind Power System

Agent P2

Wind Power System

Agent Pi

Design of Cooperative, Autonomous Multiagent Micro Wind System

House

Community


Wind Turbine System

Controller InverterLoad

(House)

3 Phase AC

DC

Automatic Switch

Transmitting Spare Power

Supplementary Power

from Other Agent

0 1 2 3 4 5 6 7 8 9 10

0

2

4

6

8

10

12

Time (Sec)

Without communication with the other agents

Pow

er (

W)

A B C

Wind Power

Load

BatteryStatusOf Charge(SOC)

d

D


Communication (Negotiation)

s1 s5

s4s2

s0

Send / Receive Request

Receive / S

endD

isagree

s3

s-1

s-2

Agent to External Environment

Agent to Agent

Query Agent / Lender Agent

Communication - ACL Messages (Propose, Request, Refuse etc.)


Wind Power System( Query Agent)

0 1 2 3 4 5 6 7 8 9 100

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Time (Seconds)

Mamdani25Test

A

B C

D E

F

Power

ON OFF ON ONOFFON: Send RequestOFF: No Request

DesiredPower

GeneratingPower


Wind Power System( Lender Agent)

0 1 2 3 4 5 6 7 8 9 100

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Time (Seconds)

Mamdani25Test

A

B C

D E

F

Power

NO YES NO NOYESNO: Decline YES: Accept

DesiredPower

GeneratingPower

B and C should not be equally considered!


FuzzifierInferenceEngine

DefuzzifierOutput Scaling

ControlRule Base

Fuzzy Logic

Input Scaling Expert Knowledge

What is Fuzzy Logic?

(http://en.wikipedia.org/wiki/Fuzzy_logic)

0.8, “ fairly cold”

0.2, “slightly warm”

0.0, “not hot”


A. d is NB Δd is PM so, output y will be NB, which means this agent doesn’t have spare electricity, and even it has to request other agents to borrow large electricity.B. d is PM Δd is PS so, output y will be PS, which means this agent has small spare electricity to lend other agents who are the lack of electricity.C. d is PM Δd is NM so, output y will be NS, which means this agent is going to the lack of electricity, so it need to request more electricity to other agents.D. d is NS Δd is NB so, output y will be NM, which means this agent is the lack of electricity, so it should request more electricity to other agents.

0 1 2 3 4 5 6 7 8 9 100

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

Time (Seconds)

Mamdani25Test

A

B C

D E

FDesiredPower

GeneratingPower

Fuzzy Logic Design


A: d is PB and Δd is PS. So, output y is set to PB, which means this agent will have more spare power.B: d is PB and Δd is NS. So, output y is set to NS, which means this agent will slowly experience the shortage of power. C: …D: …E: d is PS and Δd is PB. So, output y is set to PM, which means this agent will have more spare power.F: d is PS and Δd is NB. So, output y is set to NB, which means this agent will quickly experience the shortage of power.

Fuzzy Logic Design (Cont.)


-10 0 10 20 30 40 50 60 700

0.2

0.4

0.6

0.8

1NB NM NS ZE PS PM PB

rho

Mem

bers

hip

valu

e

: IF input d is Ai and… input Δd is Bi

THEN output y is Ci, where i = {1…m}, m = 4 and j = {1…n}, n = 7

Ai, {ZO, PS, PM, PB}Bj Cj {NB, NM, NS, ZO, PS, PM, PB}

{Negative Big (NB), Negative Medium (NM), Negative Small (NS), Zero (ZO), Positive Small (PS), Positive Medium (PM), Positive Big (PB)}

Fuzzy Logic Design (Cont.)

1

1

( )[ , ]

( )

n

i iin

ii

y U yy d d

U y


Simulation

• Agent1 will experience the shortage of power when agent2 has spare power.

• Sampling time = 10Hz• No Limitations with respect to batteries charging• No Loss of energy/electricity during transmission

Agent 1(Query Agent)

Agent 2(Lender Agent)

Negotiation


Simulation (Matlab)

Agent 1

Agent 2


Simulation (Matlab)


Simulation (Matlab)

Low pass Filter


Simulation (Agent 1)(a)

(b)

(c)

(d)

(e)

(f)

(g)

Agent 1, (a) Original power (b) Output y from the fuzzy logic (c) Request signal (1 = request, 0 = no request) (d) Needed power (e) Accept signal (1 = fully accept, 2 = partially accept, 0 = no response) (f) Lending power (g) Final power


Simulation (Agent 2)

Agent 2, (a) Original power (b) Output y from the fuzzy logic (c) Request signal (1 = request, 0 = no request) (d) Needed power (e) Accept signal (1 = fully accept, 2 = partially accept, 0 = no response) (f) Lending power (g) Final power

(a)

(b)

(c)

(d)

(e)

(f)

(g)


Conclusions

• We intended to utilize artificial intelligence (Fuzzy Logic) and multi agents system in our research to build a micro-turbine smart grids.

• By implementing two agents having the same construction, but with different amount of power, we verified that they successfully negotiate with each other so that both of them did not experience the shortage of power.

• Our research could be seen in areas where electricity is not currently connected to a grid such as developing countries and battlefields.


Current & Future Work• We have taken distributed approach in this project,

another approach that can be considered is centralized. A hybrid system can be built if we take both these approaches together.

• We are currently incorporating these findings by building a real micro grid wind system, in order to move on the next step towards our final goal of this project that is to help the people in under-developed countries to access cleaner and cheaper power thus improving their status of living.

Agent1 Agent2

Agent3

Agent4Agent5

Agent6

Central Agent


Questionsand/or

Comments?

Byung-Cheol Min

Phd StudentM2M (Machine to Machine) LaboratoryComputer and Information Technology Purdue University

http://www.purdue.edu/[email protected]

Documents

Department of Computer and Information Technology (CIT) Byung-Cheol Min 1,2, Hina Chaudhry 1,2, Eric T. Matson 1,2, J. Eric Dietz 1,3, Anthony Smith 1,2