Here we highlight the engineering challenges — and solutions — of developing and implementing microgrids and distributed energy resources. This post, part of a Microgrid Knowledge special report series, explores microgrid testing in real time through model-based engineering.
Download the full report.
Facing the challenge
Microgrids and DERs change the electrical grid in ways that create engineering challenges.
As the market for microgrids and DERs grows, the variety of technologies and vendors naturally becomes more diverse. Almost all of the new technologies employ a new class of digital power technologies to manipulate the power produced and make it possible for microgrids and DERs to function.
Consider, for example, how today’s smart grid technologies interplay with batteries. Being able to control electrical circuits digitally on a sub-microsecond time scale and being able to monitor the condition of the electric grid in real time allows batteries to help stabilize the grid. They do so by providing services such as frequency regulation and voltage support.
Digital technology also allows for the automation of microgrids and the management of resources. A modern microgrid can monitor loads and wholesale power prices and switch between the cheapest available energy source, whether it happens to be the wholesale power market or power generated from the microgrid.
Simply put, digitalization allows for fast, real-time optimization of unconventional resources like microgrids and DERs on a large scale. That expands the business models available to implement those resources, and increases the potential for their economic viability. But digitalization also brings challenges.
As vendors vie to distinguish their technologies — and their control software — from their competitors, it becomes increasingly difficult for disparate technologies to communicate effectively. A microgrid may be a great solution to the challenges of reliability and resilience. But it is, in itself, a complex machine with many moving parts. If those parts cannot “talk” to one another — and the grid — the microgrid will not be able to operate effectively.
In Massachusetts, the Air National Guard built a microgrid at Otis Air National Guard Base on Cape Cod. The microgrid is large enough to meet the energy needs of the air base using renewable energy sources. It can rapidly island, or separate, from the surrounding grid and be self-sufficient in an emergency situation or in a planned fashion. The microgrid also has a cyber secure connection to ISO New England, allowing it to sell ancillary services into the ISO’s wholesale power market as a way of generating income and making the project economic.
That robust array of capabilities made the Otis microgrid a first in many categories, but it also increased the complexity of a project that had to meet the highest levels of performance.
OTIS Microgrid site comprised of battery storage, diesel genset, wind turbine. It is the first site that was extensively tested using ultra-high fidelity HIL testbed for design and pre-commissioning; the first cyber-secure connection outside the fence; first wind-based microgrid; the first USAF dual-purpose generator; and the first behind the meter frequency regulation asset in ISO-NE. Photo courtesy of Typhoon HIL.
Shawn Doyle, vice president of facility and utility operations at the OTIS base, was the project manager for the microgrid. With a background in Air Force and aviation engineering, Doyle was used to a “prototyping approach,” where a project or system was modeled and simulated in high fidelity to test and validate concepts and designs.
“I was surprised to learn that microgrid engineering does not have a similar approach as a mandatory requirement,” Doyle says.
But Raytheon, the lead contractor on the project, contracted with Typhoon HIL to develop a high-fidelity Hardware In the Loop (HIL) testbed of the project. This prototyping platform — think of it as a microgrid “flight simulator”— supported integration of all the components of the microgrid.
Raytheon used an HIL testbed during the design and development phases before the microgrid was even built. “This significantly reduced interoperability risk,” Doyle says.
“We can show customers a career lifetime of power system events in one week of HIL factory testing.” — Scott Manson, technology director at Schweitzer Engineering Laboratories
Testing in real time
Learning how a system, such as a microgrid, operates in the context of the larger grid can be costly if those lessons are learned in the real world, that is, once the system is actually in operation.
A more effective and economical approach is to test the system extensively in a laboratory setting before deployment. Fortunately, there is a solution that allows for a system to be put through its paces before it goes live. It is known as model-based engineering.
As the name states, model-based engineering uses models — that is, computer models — of a system that connects the process of design, installation, testing, and operation of that system. This reduces the need for manual translation between different design disciplines and life cycle steps, which in turn cuts costs and the chances for error — an especially valuable attribute in this age of automation.
To reduce the risk of misoperation, the control and protection system needs to be tested for even the most unlikely events like a blackout of an entire area, a black start event, or a wildfire creating a short circuit on a transmission line.
So even though that is exactly the kind of event for which a system engineer needs to prepare, those types of events might occur only 100 times over the course of an entire career. But by modeling systems and using HIL microgrid testing technology, “in one week of testing I can show a career lifetime of events,” says Scott Manson, technology director at Schweitzer Engineering Laboratories.
One way to think about model-based engineering is that it uses fully executable models rather than diagrams on paper to visually portray the architecture of a system and the relationships between different parts of that system. Another important aspect of model-based engineering is that it uses a common language so that those responsible for different aspects of the overall system can understand what their colleagues are talking about. Also, the model can be evaluated to any specification since it is alive and executable. One can run the model and hence understand the spec better and in any operating point.
To help understand the need for model-based engineering, think of a complex system, such as a fighter jet, that comprises flight avionics, weapons and targeting systems software, and navigation and communications systems. Each is important in its own right, and all must work together. Further, the engineers that work on all the different systems, not to mention those with responsibility for the overall system, must be able to communicate via executable spec. Miscommunications in the development phase of a system escalates costs later.
NASA offers a good example of how model-based engineering can be put to use to solve problems within complex projects. Over the years, NASA’s space and communications networks evolved into three components: a deep space network, a space network and a near Earth network. Unfortunately, people working on the separate components developed their own terminology and protocols.
Employees working in the various sub-networks used various programs and document-based systems engineering, all different. To solve the problem — and modernize its space and communications networks — the space agency adopted model-based engineering. The new approach provided a platform that allowed employees working on the different components to understand the work of their colleagues and allowed workers on all aspects of the project to grasp what was happening at a system-wide level.
Automakers, too, now use model-based engineering. The average car has as many as 30 separate electrical control units and about two miles of wiring to connect its various networks. And as much as 70% of a modern automobile’s content is made up of electrical, electronic and software components. Faced with these complexities, automakers are turning to model-based engineering to facilitate design and product development and across-the-board communication in an environment where the supply chain may encompass hundreds of vendors.
Over the next few weeks, the Microgrid Knowledge series on new microgrid simulation and testing techniques will also cover the following topics:
- New Microgrid Simulation, Testing Techniques Pave Way for Future Microgrids
- Using Hardware in the Loop to Create a Microgrid Testbed
- Utilizing Hardware-in-the-Loop Testing throughout Microgrid Lifecycle
Download the full report, “Building a Better Microgrid with Hardware in the Loop,” free of charge courtesy of Typhoon HIL.
