Achieving the SmartGrid vision requires more than advanced metering infrastructure (AMI), supervisory control and data acquisition (SCADA), and advanced networking technologies. While these critical technologies provide the main building blocks of the SmartGrid, its fundamental keystone – its missing link – will be embedded software applications located closer to the edge of the electric distribution network. Only through embedded software will the true SmartGrid vision be realized.

To understand what we mean by the SmartGrid, let’s take a look at some of its common traits:

  • It’s highly digital.
  • It’s self-healing.
  • It offers distributed participation and control.
  • It empowers the consumer.
  • It fully enables electricity markets.
  • It optimizes assets.
  • It’s evolvable and extensible.
  • It provides information security and privacy.
  • It features an enhanced system for reliability and resilience.

All of the above-described traits – which together comprise a holistic definition of the SmartGrid – share the requirement to embed intelligence in the hardware infrastructure (which is composed of advanced grid components such as AMI and SCADA). Just as important as the hardware for hosting the embedded software are the communications and networking technologies that enable real-time and near realtime communications among the various grid components.

The word intelligence has many definitions; however, the one cited in the 1994 Wall Street Journal article “Mainstream Science on Intelligence” (by Linda Gottfredson, and signed by 51 other professors) offers a reasonable application to the SmartGrid. It defines the word intelligence as the “ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience.”

While the ability of the grid to approximate the reasoning and learning capabilities of humans may be a far-off goal, the fact that the terms intelligence and smart appear so often these days begs the following question: How can the existing grid become the SmartGrid?

THE BRAINS OF THE OPERATION

The fact that the SmartGrid derives its intelligence directly from analytics and algorithms via embedded intelligence applications based on analytical software can’t be overemphasized. While seemingly simple in concept and well understood in other industries, this topic typically isn’t addressed in any depth in many SmartGrid R&D and pilot projects. Due to the viral nature of the SmartGrid industry, every company with any related technology is calling that technology SmartGrid technology – all well and good, as long as you aren’t concerned about actually having intelligence in your SmartGrid project. It is this author’s opinion, however, that very few companies actually have the right stuff to claim the “smart” or “intelligence” part of the SmartGrid infrastructure – what we see as the missing link in the SmartGrid value chain.

A more realistic way to define intelligence in reference to the SmartGrid might read as follows:

The ability to provide longer-term planning and balancing of the grid; near and real-time sensing, filtering and planning; and balancing of the grid, with additional capabilities for self-healing, adaptive response and upgradeable logic to support continuous changes to grid operations in order to ensure cost reductions, reliability and resilience.

Software-based intelligence can be applied to all aspects or characteristics of the SmartGrid as discussed above. Figure 1 summarizes these roles.

BASIC BUILDING BLOCKS

Taking into consideration the very high priority that must be placed on established IT-industry concepts of security and interoperability as defined in the GridWise Architecture Council (GWAC) Framework for Interoperability, the SmartGrid should include as its basic building blocks the components outlined in Figure 2.

The real-world grid and supporting infrastructure will need to incorporate legacy systems as well as incremental changes consisting of multiple and disparate upgrade paths. The ideal path to realizing the SmartGrid vision must consider the installation of any SmartGrid project using the order shown in Figure 2 – that is, the device hardware would be installed in Block 1, communications and networking infrastructure added in Block 2, embedded intelligence added in Block 3, and middleware services and applications layered in Block 4. In a perfect world, the embedded intelligence software in Block 3 would be configured into the device block at the time of design or purchase. Some intelligence types (in the form of services or applications) that could be preconfigured into the device layer with embedded software could include (but aren’t limited to) the following:

  • Capture. Provides status and reports on operation, performance and usage of a given monitored device or environment.
  • Diagnose. Enables device to self-optimize or allows a service person to monitor, troubleshoot, repair and maintain devices; upgrades or augments performance of a given device; and prevents problems with version control, technology obsolescence and device failure.
  • Control and automate. Coordinates the sequenced activity of several devices. This kind of intelligence can also cause devices to perform on/off discreet actions.
  • Profile and track behavior. Monitors variations in the location, culture, performance, usage and sales of a device.
  • Replenishment and commerce. Monitors consumption of a device and buying patterns of the end-user (allowing applications to initiate purchase orders or other transactions when replenishment is needed); provides location mapping and logistics; tracks and optimizes the service support system for devices.

EMBEDDED INTELLIGENCE AT WORK

Intelligence types will, of course, differ according to their application. For example, a distribution utility looking to optimize assets and real-time distribution operations may need sophisticated mathematical and artificial intelligence solutions with dynamic, nonlinear optimization models (to accommodate a high amount of uncertainty), while a homeowner wishing to participate in demand response may require less sophisticated business rules. The embedded intelligence is, therefore, responsible for the management and mining of potentially billions, if not trillions, of device-generated data points for decision support, settlement, reliability and other financially significant transactions. This computational intelligence can sense, store and analyze any number of information patterns to support the SmartGrid vision. In all cases, the software infrastructure portion of the SmartGrid building blocks must accommodate any number of these cases – from simple to complex – if the economics are to be viable.

For example, the GridAgents software platform is being used in several large U.S. utility distribution automation infrastructure enhancements to embed intelligence in the entire distribution and extended infrastructure; this in turn facilitates multiple applications simultaneously, as depicted in Figure 3 (highlighting microgrids and compact networks). Included are the following example applications: renewables integration, large-scale virtual power plant applications, volt and VAR management, SmartMeter management and demand response integration, condition-based maintenance, asset management and optimization, fault location, isolation and restoration, look-ahead contingency analysis, distribution operation model analysis, relay protection coordination, “islanding” and microgrid control, and sense-and-respond applications.

Using this model of embedded intelligence, the universe of potential devices that could be directly included in the grid system includes buildings and home automation, distribution automation, substation automation, transmission system, and energy market and operations – all part of what Harbor Research terms the Pervasive Internet. The Pervasive Internet concept assumes that devices are connected using TCP/IP protocols; however, it is not limited by whether a particular network represents a mission-critical SCADA or home automation (which obviously require very different security protocols). As the missing link, the embedded software intelligence we’ve been talking about can be present in any of these Pervasive Internet devices.

DELIVERY SYSTEMS

There are many ways to deliver the embedded software intelligence building block of the SmartGrid, and many vendors who will be vying to participate in this rapidly expanding market. In a physical sense, the embedded intelligence can be delivered though various grid interfaces, including facility-level and distribution-system automation and energy management systems. The best way to realize the SmartGrid vision, however, will most likely come out of making as much use as possible of the existing infrastructure (since installing new infrastructure is extremely costly). The most promising areas for embedding intelligence include the various gateways and collector nodes, as well as devices on the grid itself (as shown in Figure 4). Examples of such devices include SmartMeter gateways, substation PCs, inverter gateways and so on. By taking advantage of the natural and distributed hierarchy of device networks, multiple SmartGrid service offerings can be delivered with a common infrastructure and common protocols.

Some of the most promising technologies for delivering the embedded intelligence layer of the SmartGrid infrastructure include the following:

  • The semantic Web is an extension of the current Web that permits machine-understandable data. It provides a common framework that allows data to be shared and re-used across application and company boundaries. It integrates applications using URLs for naming and XML for syntax.
  • Service-oriented computing represents a cross-disciplinary approach to distributed software. Services are autonomous, platform-independent computational elements that can be described, published, discovered, orchestrated and programmed using standard protocols. These services can be combined into networks of collaborating applications within and across organizational boundaries.
  • Software agents are autonomous, problem-solving computational entities. They often interact and cooperate with other agents (both people and software) that may have conflicting aims. Known as multi-agent systems, such environments add the ability to coordinate complex business processes and adapt to changing conditions on the fly.

CONCLUSION

By incorporating the missing link in the SmartGrid infrastructure – the embedded-intelligence software building block – the SmartGrid vision can not only be achieved, but significant benefits to the utility and other stakeholders can be delivered much more efficiently and with incremental changes to the functions supporting the SmartGrid vision. Embedded intelligence provides a structured way to communicate with and control the large number of disparate energy-sensing, communications and control systems within the electric grid infrastructure. This includes the capability to deploy at low cost, scale and enable security as well as the ability to interoperate with the many types of devices, communication networks, data protocols and software systems used to manage complex energy networks.

A fully distributed intelligence approach based on embedded software offers potential advantages in lower cost, flexibility, security, scalability and acceptance among a wide group of industry stakeholders. By embedding functionality in software and distributing it across the electrical distribution network, the intelligence is pushed to the edge of the system network, where it can provide the most value. In this way, every node can be capable of hosting an intelligent software program. Although decentralized structures remain a controversial topic, this author believes they will be critical to the success of next-generation energy networks (the SmartGrid). The current electrical grid infrastructure is composed of a large number of existing potential devices that provide data which can serve as the starting point for embedded smart monitoring and decision support, including electric meters, distribution equipment, network protectors, distributed energy resources and energy management systems. From a high-level
design perspective, the embedded intelligence software architecture needs to support the following:

  • Decentralized management and intelligence;
  • Extensibility and reuse of software applications;
  • new components that can be removed or added to the system with little central control or coordination;
  • Fault tolerance both at the system level and the subsystem level to detect and recover from system failures;
  • need support for carrying out analysis and control where the resources are available, not where the results are needed (at edge versus the central grid);
  • Compatibility with different information technology devices and systems;
  • Open communication protocols that run on any network; and
  • Interoperability and integration with existing and evolving energy standards.

Adding the embedded-intelligence building block to existing SmartGrid infrastructure projects (including AMI and SCADA) and advanced networking technology projects will bring the SmartGrid vision to market faster and more economically while accommodating the incremental nature of SmartGrid deployments. The embedded intelligence software can provide some of the greatest benefits of the SmartGrid, including asset optimization, run-time intelligence and flexibility, the ability to solve multiple problems with a single infrastructure and greatly reduced integration costs through interoperability.