The process involved in deploying an advanced smart grid is noteworthy in three key areas. First, the utility pursues access to an IP network architecture capable of supporting all of its communication and application needs going forward. In this build versus buy decision, the utility has three options: build and own a network or networks, lease space on a commercial network or networks, or the third choice and probably the most likely: choose a hybrid of the two. Second, the utility leverages a smart grid optimization engine that enables it to avoid the multiple integration projects required in the first generation approach above while building the advanced smart grid, but also to do much more. The smart grid optimization engine provides dynamic balancing of volt/VAR levels based on real-time data inputs from a multitude of devices. But the smart grid optimization engine also provides the ability to control the devices and the grid in real time. The smart grid optimization engine anticipates a much more complex environment, where two-way power flow occurs as the norm rather than the exception. Finally, the utility leverages standards-based digital devices in the field, substituting for proprietary devices that it had previously relied on.
A New Energy Enterprise Architecture and Smart Grid Optimization Engine
Why are a new architecture and smart grid optimization engine needed for the advanced smart grid? In order to manage complex and growing databases, more and more granular decision making is required to use that data, so utility managers need an architecture that provides access to a universal set of timely data, and visibility of system operations of the entire organization. Accuracy and timeliness depend not just on which database the data is drawn from, but when that database was refreshed and so on. Without such a management system, utility management has what one utility executive has described as “ten thousand versions of the truth.” At any particular point in time, a utility manager in an energy control center must ask, “What is real, right now?” With inadequate, incomplete, and/or out-of-date information, the definition of reality becomes skewed and highly subjective. At a minimum, management decisions that rely on a subjective interpretation of reality lose effectiveness, with risks escalating from there.
Utilities today have a fragmented view of operations derived from the silos approach and dependence on proprietary technologies that lack the ability to communicate with each other. Beyond operations, the fragmented view impacts utility system planning as well. At the beginning of each week, electric utility managers design on paper an electric network model based on anticipated conditions, which describes the current status of all the systems that comprise the distribution grid, but the planned design they envision is not maintained throughout the week. In fact, walking through an energy control center today would show multiple operational units monitoring and managing different parts of the grid, from DCS to EMS/SCADA to OMS to AMI to DR, each with a distinct view of the state of the grid provided by the stand-alone proprietary systems. It is left to the human grid operators in the control center to integrate these disparate views of the grid and make management decisions with the information they have at hand.
Features and Benefits of an Integrated Energy Ecosystem
A smart grid optimization engine, as described above, becomes an essential component of the advanced smart grid. Let us explore in more detail the features of this new visionary tool. First, a smart grid optimization engine would need to provide universal management functionality; it should be capable of running on any conceivable IP network (i.e., wired networks such as fiber and Ethernet, or wireless networks such as 3G, Wi-Fi, WiMAX, or LTE). Second, it is critical that the smart grid optimization engine provide complete security that is NIST, NERC CIP, and FIPS compliant; it should support end-to-end security, from the devices at one end, on through the software running on the devices, to the network transporting the data, down to the databases hosting the data, and to the utility NOC presenting the data. Third, the smart grid optimization engine should be capable of operating at near real-time speeds—at 100 milliseconds or less—and be able to fully support Internet Protocol (IP). Instant communication will be needed to support the functionality of an advanced smart grid.
Fourth, a smart grid optimization engine should provide superb interoperability; it should be able to support all electric devices (e.g., transformers, feeders, switches, capacitor banks, meters, inverters) from any vendor, because utilities are unlikely to settle for a reduced set of options when it comes to finding the right devices and applications to run their grids. Fifth, a smart grid optimization engine should be capable of growing to meet future needs. Such massive scalability will be needed—when the distributed energy resources now under development become commercially viable and begin deployment, millions of new devices will come under utility management purview. Finally, the smart grid optimization engine deployed to run the advanced smart grid must not only be affordable, it has to be economically competitive on a total cost of ownership basis: it must be more affordable than a dedicated multi-network solution it intends to replace and offer the lowest total cost of ownership (TCO).
Beyond features, what benefits would be expected to derive from such a smart grid optimization engine? First, the smart grid optimization engine would be expected to provide enhanced energy efficiency, not only improving distribution grid reliability and power quality but also reducing distribution line losses. Second, the smart grid optimization engine would certainly provide improved operational efficiency, based on new capabilities such as real-time monitoring and control at the NOC level, self-healing network functionality on the grid, and adaptive distribution feeders managing the distribution circuits all over the utility service territory. Third, the smart grid optimization engine would offer greater customer satisfaction, as proactive outage and restoration services were enacted, as enhanced energy products and services were made available, and as retail energy products were bundled based on targeted customer needs. Fourth, the smart grid optimization engine would contribute mightily to societal and utility goals for a gentler utility environmental impact, whether from reduced or sequestered CO2 emissions, better use of existing infrastructure, closed fossil fuel plants, or from leadership in meeting regulatory requirements. Finally, the smart grid optimization engine would provide tremendous economic benefit, as it reduced capital and operating budgets based on its more effective use of system inputs and infrastructure.
An excerpt from The Advanced Smart Grid: Edge Power Driving Sustainability by Andres Carvallo and John Cooper © 2011 Artech House, Inc. Reprinted with permission of the publisher. The book is available at www.ArtechHouse.com.
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