Demanding more intelligent grid sensors and analytics
As the technology and economics of distributed PV solar continue to improve, strong growth in U.S. capacity is expected to continue. It is projected by the year 2020, between four and five million homes will have rooftop PV solar with combined power capacity that exceeds the Three Gorges Dam in China, the world’s largest power plant. The growth will not be limited to the areas of the country with the most sustained sunshine, like California. Instead, this phenomenon will be nationwide, as payback for rooftop PV is now estimated to be around 10 years or less in the majority of American states.
A demand for more advanced sensor and analytics technology
As the U.S. grid undergoes this metamorphosis into a distributed model with a two-way power flow, the information required from the grid will expand from traditional safety and reliability data to include economic and technical data for siting feasibility and future innovation. This will not only require a proliferation of new sensors to record grid activity and events, it will demand a new generation of intelligent sensors equipped with high-resolution oscillography technology and waveform analytics in the sensor unit itself, as well as advanced analytics software on the head end for more detailed analysis.
The large scale deployment of intelligent sensors represents a significant paradigm shift in the way utilities exploit information from the distribution grid. Over the past 120 years, the utility industry has never placed major emphasis on distribution grid analytics. Relatively few sensors have been deployed here, and even today, many utilities do not collect and analyze a great deal of information from this part of the grid. As always, the number one objective of gathering information on power delivery will remain public safety. Next on the list is reliability, as ever more of our lives and commerce depend on uninterrupted electrical service.
Defining the top power delivery objectives
Whether or not the grid is one directional or two, safety will still remain the number one priority. Furthermore, as solar continues to become more attractive, utilities will be challenged to provide data on where this additional generation should be located. This is particularly important as more C&I customers adopt distributed energy resources (DER) with the potential to become major generators. Solar demand will put pressure on utilities to provide new innovation, not only in the technology to safely operate the infrastructure, but also innovation in the packaging and pricing of this distributed power generation. The quickest and most efficient way to meet these objectives is through better grid analytics.
Improved safety and reliability
Grid analytics are becoming a vital component of the Utility Industry’s public safety initiatives. They can help detect, pinpoint, and expedite the removal of hazards such as energized power lines on the ground. Although utilities have managed to continually improve restoration speeds, they’ve made less progress on understanding and predicting the underlying causes of events causing outages. Grid analytics have the capability to help utilities fix the causes of outages, not just the symptoms, which leads to significantly improved reliability.
Siting new generation
In order to safely operate a two-way grid, analytics are needed to determine the direction of the current, presence of harmonics, and other power quality issues. Without a grid analytics system, it is hard to track where power is actually flowing. This makes it very difficult to determine the best places to site additional distributed generation. If the flow of power isn’t tracked by feeder and by lateral, or perhaps even by span, then there is no data to provide solar project developers wanting to build more generation in a given location, because utilities don't know what the real flows are, and whether or not additional generation actually creates additional value. If a feeder has no distributed generation whatsoever, and significant power is injected into that feeder, most of the time the utility will be able to pay the provider of that power a fair amount of money. On the other hand, if a feeder that is already producing more power than the feeder itself needs, then it becomes more of a headache to the utility, as it now needs to put in new protective equipment to manage additional power that may have no additional value.
Lastly, without good data from analytics, it is hard to determine what innovation will have the most impact. For example, if a new type of battery is put on a feeder, good monitoring of power flows and good resolution on that feeder is required, or else the battery’s effects cannot be measured.
Requirements for a grid analytics system
Utilities still have serious bottlenecks to resolve as distributed management systems (DMS) aren’t ready for complex data, distribution analytics are immature, and the communications networks are really built for other purposes. A grid analytics system, first and foremost, must provide analytic capabilities matching the data types and structures that are generated from the distribution grid. The analytics capabilities need to be applicable not just for simple data, but also for complex data. Given the network limitations, these analytics capabilities need to be deployed locally out in the field, as well as centrally at the distribution control center. Secondly, the analytics system needs to enable data access over the network being utilized, as building a separate network for analytics purposes is generally too expensive.
Lastly, the grid system needs to interface with all devices collecting grid data, and have the ability to import all the data sets being collected in their entirety, rather than being limited to the subset of data used by SCADA and DMS for control purposes.
Grid analytics system maturity today
Intelligent grid analytics is no longer a theoretical concept. Intelligent sensors with local analytics are currently available, and have been deployed by the thousands at leading utilities, such as FPL. They have been proven to perform as designed with high reliability. Their event detection capabilities continue to mature, and the analytics capabilities are developing at a steady pace.
Michael Bauer is the president and founder of Sentient Energy, Inc.
Sentient Energy, Inc. | http://www.sentient-energy.com