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Building out the Smart Grid

What the Smart Grid Can Learn from the iPhone

Given the rapidly changing dynamics and requirements for power monitoring and control on the emerging Smart Grid, instrumentation needs to be adaptable and upgradeable via software on a versatile hardware platform.

BY BRETT BURGER, NATIONAL INSTRUMENTS

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Ten years ago, developing an application for a phone took a lot of expertise: both hardware and software. Companies like Emerson, Motorola, and Nokia had teams of talented engineers and programmers working on phone designs. From these groups sprouted a variety of phones with the standard set of applications found in the early 2000s: phone, address book, text messaging, maybe a game or two. Then iPhone with its iOS happened, and Android shortly thereafter. These platforms took the expertise of the “phone engineer” and focused on the hardware component layout, operating systems (OSs), servicing middleware, software/hardware integration, and the application programming interface (API). The API and development tools enabled software developers to become phone application designers. 

Programmers for these platforms don’t need to know what processor the phone is running or the intricacies of the OS; they just need an understanding of the platform development environment and the hooks to the hardware capabilities...and of course their market differentiating idea for the application they want to develop. The result: millions of apps, billions of dollars for the economy, more productivity and entertainment for phone owners, and venders who don’t have to service every corner of the app software market to sell a phone. The smart grid needs this. Research, technology, grid topologies, and standards are changing too fast for traditional grid devices to keep up. The smart grid needs devices built on platforms to foster the type of innovation among the domain experts that we saw with phones.

Increasing distributed renewable generation like wind and solar, aging assets, evolving standards, changing loads, and growing demand are all challenging grid operators. There is also the challenge of change, the unknown. A grid with five percent distributed solar today may have 10 percent or more just five years from now, and the growth may be from a completely different geographic region. How does that impact the ability to forecast and control a grid? Will grid operators simply need more system controllers for a larger system or will they need to have different capabilities? How do Tesla cars and Powerwalls impact neighborhood grid demand? The job of a planner at utility companies is growing more difficult as the solution requires assumptions that can be rendered quickly invalid with changing government incentives, regulations, or the globally driven price of fuel.

To further exacerbate the problem, utility companies may experience completely unique problems or sets of challenges. Arizona and Southern California have large solar installations, West Texas has large wind farms in remote areas, and England has large offshore wind farms and high-voltage DC interconnects with neighboring countries’ grids. These unique feature sets break up the market into smaller segments that can be more difficult for vendors to serve. Unique instrumentation requests from utilities are often met with requests for large non-recurring engineering costs or guarantees of high unit volumes. Even when these requests are financially viable to the utility, the development time can be lengthy.

A shift in engineering tools from purpose-built embedded systems to more open, flexible software-designed systems will spur the rate of innovation and help solve the challenges of change and uniqueness for utility companies. Grid measurement and control devices, sometimes referred to as intelligent electronic devices, need to provide a method by which grid experts can modify their functionality—API. A synchro phasor measurement unit (PMU), power quality analyzer, remote terminal unit, and digital fault recorder are all examples of common devices installed on the grid. From a hardware perspective, each of these devices connects to sensors, potential transformers and current transformers, and samples waveform data through analog-to-digital converters (ADCs). Various processing elements in the device, such as CPUs, FPGAs, or digital signal processors (DSPs), perform the waveform processing and power-related analysis. Finally, results are communicated to grid operations or the Cloud using various protocols and physical communication layers. 

A hardware teardown of these devices would show that the building block components are very similar. The difference in functionality is essentially software and firmware. Yet domain experts, outside of the ones hired by traditional vendors, have no way to access the hardware functionality. With a platform-based approach and an API, grid engineers can modify existing technology to solve unique challenges faster in a way that meets their needs without the influence of a broader market. Three specific use cases for platform design flexibility are the merging of existing functionality, better interoperability, and updating technology because of research or standards organizations (Figure 1).

Figure 1
With access to the hardware functionality of smart grid devices through a platform API, utility experts and researchers can create applications specific to their needs without having to design from the ground up.

Merging Functionality

A good example of merging functionality is large scale wind or solar. Renewable generation typically connects to the grid through DC/AC inverters that can impact the power quality of the grid by adding harmonic noise. Additional environmental data, such as solar irradiance and temperature, may also be helpful. Having a single device to measure and alert on total harmonic distortion, load, phasors, irradiance, and temperature may be advantageous to the transmission operators with remote solar, but the market for that specific device would likely be too small to merit a new product. This would leave the option to purchase multiple devices for full functionality coverage, or deal with the impacts of imperfect visibility into grid quality status.

Changing communication standards can make interoperability between devices challenging. A common communication protocol for utilities in North America is currently Distributed Network Protocol 3 (DNP3), but when it comes to the future of communication and the smart grid there are many options on the horizon. Standards like IEC-61850 and groups like the Industrial Internet Consortium (IIC) and the Smart Grid Interoperability Panel (SGIP) are spearheading the Industrial Internet of Things (lloT) trend for machine-to-machine (M2M) communication on the grid and make the future of smart grid technology look promising, albeit still a work in progress. Having the ability to modify the communication scheme can be just as important as the functionality because it helps with device interoperability and migration to new standards. A hardware platform component that has dual ports that are software programmable can communicate over the legacy and new technology protocols simultaneously.

Sometimes the technology just needs to advance to solve new problems. The functionality of a PMU is defined by the IEEE standard C37.118. This functionality was modified between the 2005 and 2011 revisions of the standard to include faster measurement capability. Renewable generation adds dynamic properties to a grid with the controllers on inverters and the fact that the wind and sun are not constant. Think gusts and clouds. Software-designed instruments built on a platform can more easily adapt and upgrade to higher performing standards, such as faster PMU report rates, because resources were not capped for optimization. With smartphones, users typically expect three to four software technology upgrades before the hardware needs a refresh. A similar model deployed to intelligent grid devices could enable utility companies to have better information with fresh software technology for 10 to 20 years.

Many instrumentation vendors already utilize platform-based design, leveraging board layouts and low-level drivers across a product line, but they stop short of an open ecosystem with an API designed for end users. The NI Grid Automation System is one example of a smart grid device designed as a platform for end user access. There are terminals to connect to the high-voltage and current utility sensors and multiple ports for communication, but most of the functionality between the two can be defined. This functionality can include waveform signal processing on a programmable FPGA with DSP slices, power analysis on a multicore processor, and communication protocols such as DNP3, C37.118, and IEC-61850. The Grid Automation System can be fully programmed from the ground up, but is shipped with a preset personality that covers standard PMU functionality. Grid owners who want a “PMU that can also…” can start from the open software personality and add on. By eliminating the need to redesign a wheel, or in this case a PMU, the next-generation PMU or PMU with custom functionality goes from design concept to field in much less time. Built around the NI CompactRIO embedded controller and programmed with NI LabVIEW software or C/C++, the Grid Automation System helps connect and process better information about unique situations within a utility grid (Figure 2).

Figure 2
The Grid Automation System from NI is designed on an open platform that lets domain experts customize the device, using LabVIEW and/or C/C++ for specific measurement needs.

The concept of programmable platform hardware democratizes the approach to smart grid technology to the benefit of all parties involved. Power engineers working for utilities have experience to increase grid uptime with the right information, but likely don’t have a lot of experience laying out ball grid array processors or developing the glue logic needed to connect an ADC to an FPGA. With a platform-based approach, power engineers can use their expertise to gain better insight into their grid. On the other side, smart grid vendors can focus more time on designing open, flexible systems, and less time trying to determine the feature set and margins required to address the top 80 percent of market applications. Power consumers, the paying public, gets a more reliable, intelligent grid that can easily integrate new generation technologies, save money on energy bills, help restore power faster after storms, and of course still charge millions of iPhones (Figure 3).

Figure 3
The PMU LabVIEW project, with standard C37.118.1-2011 functionality, is available as part of a developer library for smart grid device design.

 

 

National Instruments
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www.ni.com