SCADA technology goes back 40 years, however. Much of it is too slow for today’s challenges and does not sense or control nearly enough of the components around the grid. And although it enables some coordination of transmission among utilities, that process is extremely sluggish, much of it still based on telephone calls between human operators at the utility control centers, especially during emergencies. What is more, most programmable logic controllers and remote terminal units were developed before industry-wide standards for interoperability were established; hence, neighboring utilities often use incompatible control protocols. Utilities are operating ever closer to the edge of the stability envelope using 1960s-era controls.
The Self-Healing Smart Grid
The result is that no single operator or utility can stabilize or isolate a transmission failure. Managing a modern grid in real time requires much more automatic monitoring and far greater interaction among human operators, computer systems, communications networks and data-gathering sensors that need to be deployed everywhere in power plants and substations. Reliable operation also requires multiple, high-data-rate, two-way communications links among all these nodes, which do not exist today, plus powerful computing facilities at the control center. And intelligent processors—able to automatically reconfigure power flows when precursors to blackouts are sensed—must be distributed across the network.
Flying the grid begins with a different kind of system design. Recent research from a variety of fields, including nonlinear dynamical systems, artificial intelligence, game theory and software engineering, has led to a general theory of how to design complex systems that adapt to changing conditions. Mathematical and computational techniques developed for this young discipline are providing new tools for grid engineers. Industry working groups, including a group run by one of us (Amin) while at the Electric Power Research Institute (EPRI) in Palo Alto, Calif., have proposed complex adaptive systems for large regional power grids. Several utilities have now deployed, at a demonstration scale, smart remote terminal units and programmable controllers that can autonomously execute simple processes without first checking with a human controller, or that can be reprogrammed at a distance by operators. Much wider implementation is needed.
A self-healing smart grid can best be built if its architects try to fulfill three primary objectives. The most fundamental is real-time monitoring and reaction. An array of sensors would monitor electrical parameters such as voltage and current, as well as the condition of critical components. These measurements would enable the system to constantly tune itself to an optimal state.
The second goal is anticipation. The system must constantly look for potential problems that could trigger larger disturbances, such as a transformer that is overheating. Computers would assess trouble signs and possible consequences. They would then identify corrective actions, simulate the effectiveness of each action, and present the most useful responses to human operators, who could then quickly implement corrective action by dispatching the grid’s many automated control features. The industry calls this capability fast look-ahead simulation.
The third objective is isolation. If failures were to occur, the whole network would break into isolated “islands,” each of which must fend for itself. Each island would reorganize its power plants and transmission flows as best it could. Although this might cause voltage fluctuations or even small outages, it would prevent the cascades that cause major blackouts. As line crews repaired the failures, human controllers would prepare each island to smoothly rejoin the larger grid. The controllers and their computers would function as a distributed network, communicating via microwaves, optical fibers or the power lines themselves. As soon as power flows were restored, the system would again start to self-optimize.