Agustín Irizarry Carlos Torres Manuel Rodríguez Idalides Vergara José Cedeño Juan Jim é nez

1. Intelligent Power Routers for Distributed Coordination in Electric Energy Processing Networks Progress Report Agustín Irizarry Carlos Torres Manuel Rodríguez Idalides Vergara José Cedeño Juan Jiménez Bienvenido Vélez Marianela Santiago Miguel Vélez-Reyes Noel Figueroa Efraín O’Neill-Carrillo Alma Estremera Alberto Ramírez iprs@ece.uprm.edu

2. Outline • Background and Problem Statement • Analogy: IPRs and Data Networks • Report on project activities • Year 1 Accomplishments Summary • Year 2 Proposed activities EPNES: Intelligent Power Routers

3. State-of-the-Art Power Delivery Producers P1 P2 P3 Pn GOAL: De-centralized System Reconfiguration with Minimal Human Intervention Consumers C1 C2 C4 C3 EPNES: Intelligent Power Routers

4. System MTTR Limited by Operator Response Time Re-routing in Response to Failures Producers P1 P2 P3 Pn x x Consumers C1 C2 C4 C3 EPNES: Intelligent Power Routers

5. Re-routing in Response to Major Disturbances Producers P1 P2 P3 Pn Slow Operator Response May Cause Cascading Failures Consumers C1 C2 C4 C3 EPNES: Intelligent Power Routers

6. Re-routing in Response to Major Disturbances Producers P1 P2 P3 Pn IPRS Respond Promptly to Avoid Further Deterioration Consumers C1 C2 C4 C3 EPNES: Intelligent Power Routers

7. Our approach • De-centralized control in response to major disturbances • Intelligent Power Routers (IPR): • modular building blocks • strategically distributed over entire network • embedded intelligence • information exchange allows neighboring IPRs to coordinate networkreconfiguration • improve network survivability, security, reliability, and re-configurability EPNES: Intelligent Power Routers

8. Outline • Background and Problem Statement • Analogy: IPRs and Data Networks • Report on project activities • Year 1 Accomplishments Summary • Year 2 Proposed activities EPNES: Intelligent Power Routers

9. Distributed Data Routing Routers Data Consumer S1 S3 Data Servers R2 R1 C1 Data Network R3 R4 S2 C2 Multiple redundant paths to move data between computers EPNES: Intelligent Power Routers

10. Data Packets Major Disturbance Re-routing in Response to Major Disturbances Data Consumer S1 S3 R2 R1 Data Servers C1 Data Network R3 S2 R4 C2 EPNES: Intelligent Power Routers

11. Data Packets Major Disturbance Re-routing in Response to Major Disturbances Data Consumer S1 S3 R2 R1 Data Servers C1 Data Network R3 S2 R4 C2 EPNES: Intelligent Power Routers

12. How are power delivery systems different from computer networks? • Energy transmission (not data) • Must match generation to demand at all times • No buffers • Hard to get rid of excess energy We must deal with the laws of Physics! EPNES: Intelligent Power Routers

13. Outline • Background and Problem Statement • Analogy: IPRs and Data Networks • Report on project activities • Year 1 Accomplishments Summary • Year 2 Proposed activities EPNES: Intelligent Power Routers

14. Project Organization Education IPR Architecture Distributed Control Models Economics Education Education Restoration Models IPR Protocols Risk Assessment Education EPNES: Intelligent Power Routers

15. IPR Protocols Restoration Models Restoration Models and IPR Protocols • Use the Power System Restoration (PSR) problem, an extreme condition, as starting point to address the system reconfiguration problem. • Use PSR problem global (centralized) solution as benchmark • Develop communication and data protocols that allow the implementation of different de-centralized restoration strategies EPNES: Intelligent Power Routers

16. Restoration Models Power System Restoration (PSR) • Goal: • rebuild a stable electric system • restore all unserved loads • Approach: • Apply particle swarm optimization (PSO) to solve PSR • Optimization problem: • minimize the amount of unserved loads at each stage • power flow constraints • feasible bounds on state and control variables • capacity limits on lines and transformers • only one switching operation per stage EPNES: Intelligent Power Routers

17. Restoration Models Particle swarm optimization (PSO) method • Emerging Evolutionary Computation (EC) technique [Kennedy 1995] • Based on "flocking behavior" of animals • In PSO individuals move around in a search space looking for an optimal solution based on their current position and on the best position within the flock. Continuous variables Binary variables EPNES: Intelligent Power Routers

18. Restoration Models Power System Restoration:Example Test System and Results: • Total load served increased through the stages. • At each stage, all the control and state variables remained within their feasible limits and the power balance constraints were satisfied. • The restoration path was established and all loads were successfully served. 75% 100% Restoration Completed 100% 50% 25% 50% 100% 50% WSCC Nine-Bus Test System EPNES: Intelligent Power Routers

19. IPR Protocols De-Centralized Communication & Control Protocols • Goal: • Develop Communication Protocols to implement a System Restoration Algorithm • Approach: • Use a graph model for the power network with IPRs • Optimization problem: • minimize the amount of unserved loads based on priority [Nagata et. al. 2002] EPNES: Intelligent Power Routers

20. Src 1 Src 2 Src 3 Link 1 Link 2 Link 3 Bus 1 Bus 2 IPR 1 IPR 2 Link 4 Link 5 Link 6 Bus 4 Bus 3 IPR 3 IPR 4 Link 7 Link 8 Snk 1 Snk 2 Control Messages IPR Protocols Modeling a Power Network As a Graph • IPR model: • Vertices – IPRs on buses • Edges – branches between buses • Weight – power flow • Edges have Priority/ • Reliability measure EPNES: Intelligent Power Routers

21. Link 1 Link 2 Link 3 Bus 1 Bus 2 Src 1 Src 2 Src 3 IPR 1 IPR 2 Link 4 Link 5 Link 6 Bus 4 Bus 3 Link 7 Link 8 IPR 3 IPR 4 Snk 1 Snk 2 IPR Protocols Restoration in Electrical Energy Network Featuring Intelligent Power Routers (IPRs) System going down Restoration Process Normal State Table 1. Priority and Reliability — Normal State Message — Deny Request — Response Status — Request Power — Request Status —Affirmative Response EPNES: Intelligent Power Routers

22. Risk Assessment Risk Assessment • Goal: • Measure the change in reliability of a power system operated with and without IPRs. • Approach: • Use an existing method • Well-Being indices [Billinton et.al.] • Risk Framework [McCalley et.al.] • Need failure probability data EPNES: Intelligent Power Routers

23. IPR failure mechanism Risk Assessment • No data available on IPR failure probability • Need to understand failure mechanisms • Computer Hardware • Power Hardware • Literature search well under way for both • Software • Data Routers info will be used to make an initial estimate on failure probability. Intelligence Software Data Router Comp Hardware Switch Power Hardware IPR EPNES: Intelligent Power Routers

24. Education Education Education Education Year-to-Date Accomplishments • Proposed: • Development of economics and ethics modules • Achieved: • Developed a module on ethics • Offered two ethics seminars • Ethical and Social Implications in Engineering • Integrating Ethics to the Curriculum • Proposed a new EE Course on economic issues • Started collaboration with Social Sciences (modules to assess student perceptions) • Introduced IPR concept in graduate courses • Offered IPRs seminars • integration of research into undergraduate education • recruit students • disseminate our results EPNES: Intelligent Power Routers

25. Central Controller Distributed Control Models DC Zonal Electric Distribution System (DCZEDS) with Centralized Controller • Controller Characteristics • Global State Information • Controller decisions can achieve global optimality. • Reliability issues. EPNES: Intelligent Power Routers

26. Controller Controller Controller Distributed Control Models DCZEDS with Distributed Control • Controller Characteristics • Local State Information • Quality is an issue in controller decisions. • Potential to improve survivability and reliability. EPNES: Intelligent Power Routers

27. Distributed Control Models Intelligence in the IPR • Flat system: no supervisory control • Solving a dynamic optimization (or control) problem • Different Concepts to be Explored • Agents • Biologically collaborative schemes EPNES: Intelligent Power Routers

28. Sensor Switching Input Commands Interfacing ICCU Circuits Intelligent Power Router IPR Architecture Proposed architecture for the Intelligent Power Router Power Network Energy Sensors and Flow Control Devices Programmable Intelligent Communication and Control Unit EPNES: Intelligent Power Routers

29. IPR Architecture A Simple Switch-based IPR System Source Source • Upon failure of a source, IPR decides which load to serve based on latest dynamic priorities • Decision can be any computable function • More complex configurations possible by modular composition of multiple levels of IPRs • Future IPRs based on more complex power flow control devices (e.g. FACTS) sensor sensor IPR Transfer Switch Transfer Switch Load Load EPNES: Intelligent Power Routers

30. IPR Architecture Simulating the Simple IPR System Source Current Sensor Power Lines Transfer Switch Load EPNES: Intelligent Power Routers

31. Outline • Background and Problem Statement • Analogy: IPRs and Data Networks • Report on project activities • Year 1 Accomplishments Summary • Year 2 Proposed activities EPNES: Intelligent Power Routers

32. What we accomplished in year 1 • Developed first generation IPR software models • Developed first generation communication and data exchange mechanism for IPR • Studied the DC Zonal Electric Distribution System (DCZEDS) • Studied the power system restoration problem, using particle swarm optimization • Started to determine IPR failure modes thru analogy to data routers • Developed economics and ethics modules • One accepted paper, two under review EPNES: Intelligent Power Routers

33. What we promise for year 2 … • Disseminate results from iteration 0 • Design of alternative IPR control algorithms • Perform simulations for preliminary reliability assessment on IPR-based system • Design of second generation of IPR software model • Evaluate alternative IPR control algorithms • Use economics and ethics modules in electrical engineering courses and use assessment tools • Develop a short course for non-power engineering majors EPNES: Intelligent Power Routers

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