Byung-Cheol Min 1,2 , Hina Chaudhry 1,2 , Eric T. Matson 1,2 , J . Eric Dietz 1,3 , Anthony Smith 1,2

1. Rural Energy Security Using Autonomous Micro-turbine Smart Grids Byung-Cheol Min1,2, HinaChaudhry1,2, 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 minb@purdue.edu IEEE Rural Electric Power Conference 10-13 April 2011 Chattanooga, TN, USA

2. 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

3. What is Smart Grid? (http://venturebeat.com/2011/02/01/how-secure-is-the-smart-grid/)

4. Motivation • Control • Balance • Manage • The whole power system + + + + (http://good-energy.typepad.com) (b) Cooperative agents for management of wind power system (a) House wind turbine The aim of this project is to develop a cost-effective1, scalable2, portable3 , and plug-and-play4 wind power systemand design cooperative agents for its management.

5. Design of Cooperative, Autonomous Multiagent Micro Wind System House Community

6. Wind Turbine System

7. Communication (Negotiation) Communication - ACL Messages (Propose, Request, Refuse etc.) Query Agent / Lender Agent

8. Wind Power System( Query Agent) F C B Desired Power D E Generating Power A Power OFF ON OFF ON ON ON: Send Request OFF: No Request

9. Wind Power System( Lender Agent) F C B Desired Power D E Generating Power A B and C should not be equally considered! Power YES NO YES NO NO NO: Decline YES: Accept

10. What is Fuzzy Logic? 0.8, “ fairly cold” 0.2, “slightly warm” 0.0, “not hot” (http://en.wikipedia.org/wiki/Fuzzy_logic) Expert Knowledge

11. Fuzzy Logic Design F C B Desired Power D E Generating Power A A. dis NB Δdis 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. dis PM Δdis 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. dis PM Δdis 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. dis NS Δdis 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.

12. Fuzzy Logic Design (Cont.) A: d is PB and Δdis PS. So, output y is set to PB, which means this agent will have more spare power. B: d is PB and Δdis 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 Δdis PB. So, output y is set to PM, which means this agent will have more spare power. F: d is PS and Δdis NB. So, output y is set to NB, which means this agent will quickly experience the shortage of power.

13. Fuzzy Logic Design (Cont.) : 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} BjCj {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)}

14. Simulation Negotiation Agent 1 (Query Agent) Agent 2 (Lender Agent) • 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

15. Simulation (Matlab) Agent 1 Agent 2

16. Simulation (Matlab)

17. Simulation (Matlab) Low pass Filter

18. Simulation (Agent 1) 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

19. 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

20. 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.

21. 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.

22. Questionsand/orComments? Byung-CheolMin Phd Student M2M (Machine to Machine) Laboratory Computer and Information Technology Purdue University http://www.purdue.edu/m2m minb@purdue.edu