Six Questions Energy Utilities Need to Ask to Future-Proof Systems
Not all big data is created equal, especially when it comes to implementing smarter grid technologies in the energy industry. Many analytics discussions revolve around how big data can be used to make energy transmission more efficient, but few people mention how quickly some data can become too stale (old) to be useful in operating the distribution grid. As the distribution grid adds more embedded, or intelligent, sensors and distribution management systems become more common in the industry, one of the big issues will be identifying what data has to be returned by what time to be useful for grid operations. This latency issue on data will become more critical as controls are deployed that allow real changes in how the grid operates.
Here are six questions you should ask yourself in the planning process to help you avoid latency pitfalls and future-proof your operations for the era of big data (with examples):
1. If your interest is strictly conservation of voltage, how much time do you need to return data from the field? Likely your needs are not that time sensitive, therefore you can conclude that almost any communications system with almost any latency and bandwidth will support the needs.
2. If your need remote system frequency control using distributed storage, latency can be a big issue. Ask yourself what tasks need to be done autonomously and what can be accomplished centrally? Using storage for frequency control will always be autonomous, since the time from measurement to decision is so short.
3. If your need is complex remote switching orders, latency and deterministic routes both play into the equation. Ask yourself, how do we ensure that the orders arrive in the right sequence and are executed in the right sequence with the right timing, while providing the operator with the right confirmation data? All it takes is one mistake to drop the load on a circuit.
4. Where is the latency occurring in your communications?
5. Where is the latency occurring in your analysis? Will this increase in the future when there is more data to deal with? This will help you determine where to place your resources in the future.
6. What applications need to access your data and the analytics? Does there need to be a hierarchy of priorities for access? As we anticipate an operations landscape with multiple applications looking for data, determine the specific devices and data you will be giving priority to. Be wary of systems that have too high of a latency rate for applications to be successful.
Some lessons are already being learned about prioritization. For day to day operations, only a small number of revenue meters are useful. They tend to be in locations that have poor power quality, voltage issues, or have large loads that are variable. Some are residential – in fact typically there is 1 meter in 1,000 to 1 meter in 10,000 that are useful for day to day operations. Relays that can provide complete fault records are also becoming very important, since typically the relay can pinpoint the fault location in the first zone and in many cases into the second zone. This pinpointing can save large amounts of patrol time. These relays can also help pinpoint the equipment to do maintenance on when the relay sees a fault but does not trip. Bushings and other components that may be failing be located from these fault records, allowing planned replacement before total failure occurs.
Getting this kind of data into and out of the analytics systems rapidly is the key to being able to capture the value from this data and the analytics developed for each application.
Resources for your planning
In the forthcoming EPRI report on the benefits of AMI, more than 90 different uses of Advanced Metering Infrastructure (AMI) data have been identified. Of those around 20 are operational in nature and have latency issues to address. In a broader discussion of grid modernization, even more applications have been identified that have latency and prioritization issues. Early decisions on what data is needed, how it should be prioritized, and what the latency is, will have big impacts on the overall systems design and offer the ability to avoid massive re-work or re-investment.
Don’t forget that there are in some cases regulatory requirements for archiving data that is operational in nature in your planning.