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Restarting the grid after a total failure is trickier than it may appear
Edd Gent is a freelance science and technology writer based in Bangalore, India. His writing focuses on emerging technologies across computing, engineering, energy and bioscience. He's on Twitter at @EddytheGent and email at edd dot gent at outlook dot com. His PGP fingerprint is ABB8 6BB3 3E69 C4A7 EC91 611B 5C12 193D 5DFC C01B. His public key is here. DM for Signal info.
Restoring power quickly after a major blackout can mean the difference between life and death, but cold starting an entire electrical grid is a complex and delicate process. A hybrid computer model from Sandia National Laboratories that combines optimization, physical simulations and cognitive models of grid operators promises to come up with a fast and reliable plan to get the lights back on.
While power outages are always disruptive, they typically only impact smaller portions of the overall grid. A complete loss of power over the entire network is much more serious, and requires operators to effectively jump start the grid with so-called “black start” generators. This involves a complicated balancing act to avoid mismatches between energy generation and consumption, as different sections of the grid are gradually brought back online. Get it wrong and the grid can collapse again.
“You wind up having to basically feel around in the dark to make sure, ‘Does reality match up with what all of my data tells me?’”
—Kevin Stamber, Sandia National Laboratories
Such events are thankfully rare, says Kevin Stamber, who led the project at Sandia, but there have been some close calls in recent years. When Texas’s power system experienced major disruptions during winter storms last February, operators were just minutes away from a complete grid failure that could potentially have taken months to resolve, he says. With climate change increasing the frequency of extreme weather events, and the growing threat of cyber-attacks on critical power infrastructure, the danger is only likely to increase.
That prompted Stamber’s team to come up with a new method for creating black start plans better able to cope with the often unpredictable behavior of real-world power systems. That’s no easy feat though, he says. “It’s a very, very delicate process on a very large system,” he says. “They [black starts] are complex, challenging, difficult to solve and require a substantial computational commitment to be able to get to a solution.”
The gold standard approach treats a black start as an optimization problem, aimed at working out the best order in which to restore different grid components, such as generators, substations and power lines. Existing techniques tend to assume that the operator has full visibility and control over the grid though, which often isn’t the case.
In a blackout, key components may be damaged, says Stamber, and operators may not have a full picture of what is available to them. And while utilities are likely to have a rough idea what the load on different parts of the grid should be, there are no guarantees. “You wind up having to basically feel around in the dark to make sure, ‘Does reality match up with what all of my data tells me?’” says Stamber.
That prompted the team to pair a cutting-edge optimization approach created by researchers at Lawrence Livermore National Laboratory and the University of California, Berkeley with additional modules designed to simulate how the grid could react to the restoration plan, and how the operators implementing it would behave.
Researchers envisage something along the lines of a Choose Your Own Adventure book, but for grid operators.
The optimization model’s goal is to restore power as quickly as possible, while ensuring that the load on the grid is stable and doesn’t cause another failure. It produces a restoration schedule outlining what order to power up different generators and when to connect different portions of the grid. This is also checked against a physical model of power flow to make sure each step is feasible.
This restoration plan is then fed to a cognitive model of a grid operator built using the ACT-R framework, which makes it possible to simulate human decision making. The model was built by encoding expert knowledge about how to carry out key steps involved in grid restoration, and is able to read the restoration plan and use a simulated console to implement it.
However, the console is also hooked up to a dynamic physics-based simulation of the grid, which is designed to mimic how the network responds to the operators actions, sometimes in hard to predict or challenging ways. The cognitive model is presented with information on the grid’s response through the simulated console, and, if steps from the restoration plan cause any stability issues, it can take corrective action before moving onto the next step.
By simulating how an operator might deploy a restoration plan and react to the grids behavior, Stamber hopes to create plans much more tolerant of unexpected behavior. He envisages something along the lines of a Choose Your Own Adventure book, but for grid operators. “There are certain points along the way where things don’t go the way you’re expecting, and you wind up in a different portion of the book,” he says.
The idea of incorporating the cognitive behavior of the operator is an interesting one, says Saifur Rahman, a professor of electrical engineering at Virginia Tech and 2023 IEEE President and CEO. But he points out that in a real-world system control center there are multiple operators with different perspectives interacting with each other. Also, so far, the team has only tested the approach on the IEEE Reliability Test System (RTS-96), which is small compared to a real-life power system, Rahman notes. “In order to be credible in a real situation I would have liked to see it tested on a 20,000 or 30,000 node system,” he says.
Part of the reason the team didn’t, says Stamber, is that they simply ran out of time and budget for the project. But he also thinks their approach does need some work to make it more tractable on larger systems, perhaps by breaking the optimization problem up into smaller sub-problems that are less computationally intensive. Either way, the team is now looking for potential utility partners who can work with them on applying their techniques to more realistic problems.
Engineers at EPFL used simulation to design photonic devices for enhanced optical network speed, capacity, and reliability
This sponsored article is brought to you by COMSOL.
The modern internet-connected world is often described as wired, but most core network data traffic is actually carried by optical fiber — not electric wires. Despite this, existing infrastructure still relies on many electrical signal processing components embedded inside fiber optic networks. Replacing these components with photonic devices could boost network speed, capacity, and reliability. To help realize the potential of this emerging technology, a multinational team at the Swiss Federal Institute of Technology Lausanne (EPFL) has developed a prototype of a silicon photonic phase shifter, a device that could become an essential building block for the next generation of optical fiber data networks.
Using photonic devices to process photonic signals seems logical, so why is this approach not already the norm? “A very good question, but actually a tricky one to answer!” says Hamed Sattari, an engineer currently at the Swiss Center for Electronics and Microtechnology (CSEM) specializing in photonic integrated circuits (PIC) with a focus on microelectromechanical system (MEMS) technology. Sattari was a key member of the EPFL photonics team that developed the silicon photonic phase shifter. In pursuing a MEMS-based approach to optical signal processing, Sattari and his colleagues are taking advantage of new and emerging fabrication technology. “Even ten years ago, we were not able to reliably produce integrated movable structures for use in these devices,” Sattari says. “Now, silicon photonics and MEMS are becoming more achievable with the current manufacturing capabilities of the microelectronics industry. Our goal is to demonstrate how these capabilities can be used to transform optical fiber network infrastructure.”
Optical fiber networks, which make up the backbone of the internet, rely on many electrical signal processing devices. Nanoscale silicon photonic network components, such as phase shifters, could boost optical network speed, capacity, and reliability.
The phase shifter design project is part of EPFL’s broader efforts to develop programmable photonic components for fiber optic data networks and space applications. These devices include switches; chip-to-fiber grating couplers; variable optical attenuators (VOAs); and phase shifters, which modulate optical signals. “Existing optical phase shifters for this application tend to be bulky, or they suffer from signal loss,” Sattari says. “Our priority is to create a smaller phase shifter with lower loss, and to make it scalable for use in many network applications. MEMS actuation of movable waveguides could modulate an optical signal with low power consumption in a small footprint,” he explains.
The MEMS phase shifter is a sophisticated mechanism with a deceptively simple-sounding purpose: It adjusts the speed of light. To shift the phase of light is to slow it down. When light is carrying a data signal, a change in its speed causes a change in the signal. Rapid and precise shifts in phase will thereby modulate the signal, supporting data transmission with minimal loss throughout the network. To change the phase of light traveling through an optical fiber conductor, or bus waveguide, the MEMS mechanism moves a piece of translucent silicon called a coupler into close proximity with the bus.
Figure 1. Two stages of motion for the MEMS mechanism in the phase shifter.
The design of the MEMS mechanism in the phase shifter provides two stages of motion (Figure 1). The first stage provides a simple on–off movement of the coupler waveguide, thereby engaging or disengaging the coupler to the bus. When the coupler is engaged, a finer range of motion is then provided by the second stage. This enables tuning of the gap between the coupler and bus, which provides precise modulation of phase change in the optical signal. “Moving the coupler toward the bus is what changes the phase of the signal,” explains Sattari. “The coupler is made from silicon with a high refractive index. When the two components are coupled, a light wave moving through the bus will also pass through the coupler, and the wave will slow down.” If the optical coupling of the coupler and bus is not carefully controlled, the light’s waveform can be distorted, potentially losing the signal — and the data.
The challenge for Sattari and his team was to design a nanoscale mechanism to control the coupling process as precisely and reliably as possible. As their phase shifter would use electric current to physically move an optical element, Sattari and the EPFL team took a two-track approach to the device’s design. Their goal was to determine how much voltage had to be applied to the MEMS mechanism to induce a desired shift in the photonic signal. Simulation was an essential tool for determining the multiple values that would establish the voltage versus phase relationship. “Voltage vs. phase is a complex multiphysics question. The COMSOL Multiphysics software gave us many options for breaking this large problem into smaller tasks,” Sattari says. “We conducted our simulation in two parallel arcs, using the RF Module for optical modeling and the Structural Mechanics Module for electromechanical simulation.”
The optical modeling (Figure 2) included a mode analysis, which determined the effective refractive index of the coupled waveguide elements, followed by a study of the signal propagation. “Our goal is for light to enter and exit our device with only the desired change in its phase,” Sattari says. “To help achieve this, we can determine the eigenmode of our system in COMSOL.”
Figure 2. Left: Light passes from left to right through a path composed of an optical bus and a coupled movable waveguide. Right: Cross-sectional slices of a simulated light waveform as it passes through the coupled device. By adjusting the distance between the two optical elements in their simulation, the EPFL team could determine how that distance affected the speed, or phase, of the optical signal.
Images courtesy EPFL and licensed under CC BY 4.0
Figure 3. Simulation showing deformation of the movable waveguide support structure. The thin elements that suspend the movable waveguide will flex in response to an applied voltage.
Image courtesy EPFL and licensed under CC BY 4.0
Figure 4. Optical simulation (left) established the vertical distance between the coupler and waveguide that would result in a desired phase shift in the optical signal. Electromechanical simulation (right) determined the voltage that, when applied to the MEMS mechanism, would move the coupler waveguide to the desired distance away from the bus.
Images courtesy EPFL and licensed under CC BY 4.0
Along with determining the physical forms of the waveguide and actuation mechanism, simulation also enabled Sattari to study stress effects, such as unwanted deformation or displacement caused by repeated operation. “Every decision about the design is based on what the simulation showed us,” he says.
The goal of this project was to demonstrate how MEMS phase shifters could be produced with existing fabrication capabilities. The result is a robust and reliable design that is achievable with existing surface micromachined manufacturing processes, and occupies a total footprint of just 60 μm × 44 μm. Now that they have an established proof of concept, Sattari and his colleagues look forward to seeing their designs integrated into the world’s optical data networks. “We are creating building blocks for the future, and it will be rewarding to see their potential become a reality,” says Sattari.