1. Distributed Real-Time Systems for the Intelligent Power Grid Prof. Vincenzo Liberatore

2. Intelligent Power Grid Situational awareness by means of time-stamped data collection, real-time wide-area visualization, and data integrity within and outside an operator’s own area. Improvement of the quality of models and simulations by continuously matching models with measured data, for example to formulate and develop improved provisioning and contingency plans, and predictive models for security assessment and enhancement. Timely and accurate information dissemination to all key stakeholders, including state and local officials, as wells as customer communication that is more scalable than one-on-one telephone calls. Proactive operations of facilities. Real-time actions and distributed control of protection devices to prevent cascading failures or for the graceful degradation of user service based on service priorities, etc. Real-time wide-area control to minimize power generation over-provisioning. Context-dependent models and control of massive and cascading failures via predictable component interactions to achieve robustness, fault-tolerance, or graceful performance degradation. Large-scale distributed real-time embedded software development according to the best practices in Software Engineering. Integration of legacy systems as well as the plug-and-play introduction of novel components and solutions. Support for ubiquitous alternative energy generation systems and the seamless integration of these systems into utility operation. Market dynamics, for example, in the context of power routing transactions and regulatory issues

3. Distributed Real-Time Embedded Systems Communication Networks Connect computers, embedded systems sensors, actuators, controllers, signal processors, synchronophasors Connect computers, embedded systems sensors, actuators, controllers, signal processors, synchronophasors Distributed Systems Connectivity, programmability, scalability Connectivity, programmability, scalabilityReal-Time Predictable timing of computation Predictable timing of computation Critical for real-time monitoring and control Critical for real-time monitoring and control

4. Distributed Real-Time and Power Distributed Real-Time Embedded systems provide underpinning of Communication Communication Software Development Software Development Critical for objectives Situational awareness Situational awareness Distributed software components monitor phase angles and other quantities of interest Report to visualization centers, logging facilities Support cooperative work On-line diagnostics On-line diagnostics Off-line simulations, forensics Off-line simulations, forensics Distributed control Distributed control Automatically close the feedback loop

5. Short-Term Challenge: Co-Simulation Simulate jointly the computer network and the grid Expertise Computer Networks simulations (Prof. Liberatore) Computer Networks simulations (Prof. Liberatore) Hybrid System simulations (Prof. Branicky) Hybrid System simulations (Prof. Branicky) Previous work Ns2 and differential equation solver [BLP03, etc.] Ns2 and differential equation solver [BLP03, etc.] Oak Ridge National Labs, etc. Oak Ridge National Labs, etc. Future work Co-simulation of computer networks and power systems Co-simulation of computer networks and power systems Integration with Modelica Formulation of objectives and scenarios Formulation of objectives and scenarios

6. Co-Simulation Methodology Simulation languages Bandwidth monitoring VisualizationNetwork dynamicsPlant output dynamics Packet queueing and forwarding Co-simulation of systems and networks Plant agent (actuator, sensor, …) Router Controller agent (SBC, PLC, …) [Branicky, Liberatore, Phillips: ACC’03]

7. Medium-Term Challenges (I) Real-Time Networked Control (I) Close-feedback loop in real- time over a network Close-feedback loop in real- time over a network Network Quality-of-Service (QoS) Network Quality-of-Service (QoS) Prevent timing failures E.g., fully-distributed QoS [L04a] Allocation of network resources [ABLP06] Allocation of network resources [ABLP06] Depends on system requirements (stability, performance) Fully distributed, asynchronous, scalable Dynamic and flexible Optimization approach

8. Medium-Term Challenges (II) Real-Time Networked Control (II) Application adaptability Application adaptability End-point adapts to timing failures [L06] In-network synchronization (IEEE PTP) [B06] In-network synchronization (IEEE PTP) [B06] Real-Time Secure Management Real-Time Secure Management

9. Play-Back Sequence number time Packet generation Play-back Packet arrival

10. Medium-Term Challenges (III) Software Engineering Large-scale distributed real-time embedded systems Large-scale distributed real-time embedded systems Functional scalability [L04] Software development platforms and middleware Software development platforms and middleware E.g., RT Corba and power applications Multi-agent software systems [ACKRNL03] Integration of software, protocols, and standards Integration of software, protocols, and standards

11. Emulation Application benchmark National Lambda Rail (or GENI) “NLR is planned to be capable of supporting both production and experimental networks. “NLR is planned to be capable of supporting both production and experimental networks. Not a single network or a single test bed but facilities to build multiple networks and multiple test beds at all of layers 1-3 including optical, switched, and routed. Not a single network or a single test bed but facilities to build multiple networks and multiple test beds at all of layers 1-3 including optical, switched, and routed. Goal is to have both persistent and flexible infrastructure(s) Goal is to have both persistent and flexible infrastructure(s) Foster network research” Foster network research” Support QoS Support QoS Real-Time Overlay Support end-to-end RT S&R Support end-to-end RT S&R

12. Conclusions Intelligent Grid Distributed Real-Time Embedded Systems Immediate need Co-simulation Co-simulation Long-term needs Software Engineering Software Engineering Networked Control Networked Control