Many factors are changing the way electric grids operate: the rise of renewable energy sources, regulations, and smart technologies that drive intelligent devices in the home or along the grid. All of these factors have influenced organizations to re-examine the technologies that support the management and operation of the grid as it gets smarter.
Until recently, electric grids have been a one-way, generation-to-consumption conduit. However, technologies have now emerged that enable two-way communications, creating the possibility of a dialogue between the consumer and the supplier. These technologies started with the development of agreed-upon protocols for communications that provide a basic infrastructure to make the grid smarter.
The two-way flow of information is an important aspect of on-site residential power generation. Major industrial plants have long been able to feed excess capacity back into the grid in the form of spare industrial power. But now, consumers who have who have on-site generation in the form of small wind turbines or solar panels can also help balance load by selling extra electricity back to utilities.
These advancements are resulting in a lot of additional data, which needs to be managed by utilities. Choosing the right technology or technologies to do that may make the difference in realizing a significant return on investment. Choices range from high-powered, generic data analytics tools from major IT vendors to energy-data management tools from specialist suppliers. Both are capable of making sense of a data stream and presenting it in dashboards and spreadsheets that are dynamically updated to give a moment-by-moment view of energy supply and demand.
The closer connection to and greater involvement with energy consumers has implications for the location-based data maintained by infrastructure companies. A schematic view of the grid showing only the electrically significant connection and switching points is no longer adequate, as utilities need to understand the interaction with each consumer.
Utility managers need to know where a far greater number of occasional, distributed generation and feed-in points - whether from industrial sites or rooftop solar panels - connect with the grid and the impact they have on the overall flow. Location has always been crucial to the operation of a power grid, whether calculating losses, balancing loads or planning new extensions. The latest changes create demand for technology that has location as standard, and which will support new devices, software and services throughout the system - from the generation plant to the consumer's meter and back again.
Location-based software and geospatial technology need to be able to meet this manifestation of big data head-on. In an interconnected grid, isolating a section where load can be balanced by reducing the feed to a commercial property in order to maintain adequate coverage elsewhere can make the difference between uninterrupted supply and an unplanned outage. This requires a combination of demand-management tools and location-aware services. Software suppliers are addressing these issues in subtly differing ways, but they all have a focus on data. A recent survey showed that most utilities prioritized smart grid readiness but admitted that their network data was not maintained in a timely way.
Without an up-to-date view of the "as-built" grid, many tasks - from routing work crews to the right depot when delivering new meters to analyzing consumers' behavior - become problematic. This is compounded by considering the introduction of opt-out clauses for consumers who do not want smart meters. Utilities have to keep records of where traditional meters are still in operation and where smart meters are installed but are not active, in addition to maintaining provisions for manual meter reading and alternative billing systems. It is clear that data readiness is a large part of being able to fully utilize the tools available, because without an accurate record as a starting point, there is risk of compounding the problem as new and more accurate data is laid over error prone, historic data.
Situations and needs vary across the globe, and one size does not fit all. The need for a smarter grid in the developed world are different from what is important in emerging markets. In parts of the Asia Pacific and Latin America, for example, basic mapping of the grid takes priority. But the grid must be built to accommodate the data explosion that comes with smart grid; otherwise, the investment may not pay off.
In both developed and emerging markets, it is important to remember that geospatial tools are also important in the design and extension of the grid, as well as in the grid's operation. It is much easier to record the location of an asset as it is being installed than to revisit and record it later. Having a single tool that can, for example, handle the detail of grid design, as well as the spatial analysis applied when assessing consumer demand, is not such a stretch.
All of these considerations are crucial and should be considered together, because the cleverest engineering application is rendered useless if there is little confidence in the data. In addition, incumbent geospatial and data management tools may already address some of these considerations, but the approach to smart grid support should be a big part of the mix to ensure optimum development and operations.
Christine Easterfield is a principal consultant for Cambashi, a global analyst and market consulting firm for the energy and utilities industries. Her experience includes geospatial asset management for utilities, as well as a background in software programming, training and consulting.
