Distributed Real-Time Systems for the Intelligent Power Grid

Prof. Vincenzo Liberatore

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

Distributed Real-Time and Power

Distributed Real-Time Embedded systems provide underpinning of   Communication Software Development Critical for objectives   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 Off-line simulations, forensics Distributed control Automatically close the feedback loop

Short-Term Challenge: Co-Simulation

Simulate jointly the computer network and the grid Expertise   Computer Networks simulations (Prof. Liberatore) Hybrid System simulations (Prof. Branicky) Previous work   Ns2 and differential equation solver [BLP03, etc.] Oak Ridge National Labs, etc.

Future work   Co-simulation of computer networks and power systems Integration with Modelica Formulation of objectives and scenarios

Co-Simulation Methodology

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

Medium-Term Challenges (I)

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

Medium-Term Challenges (II)

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

Sequence number

Play-Back

Packet generation Packet arrival Play-back time

Medium-Term Challenges (III)

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

Conclusions

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