As with most new technologies, utilities should be prepared to face implementation challenges. Numerous resources must work together for DVVC to achieve slated goals. Data must be aggregated from different platforms — such as geographic information systems (GIS), metering data, system models, EMS, DMS and outage management system (OMS) — to a centralized location for analysis. Engineers, operators and managers all need to understand how this operational technology may affect their business. The initial design will take additional engineering resources for implementation, and after installation, the maintenance and updating of the system will require training a team of dedicated resources.
The system and feeder properties themselves can be some of the biggest challenges. As discussed earlier, the biggest of these is existing low voltage at the EOL. For example, a feeder that supplies 5,000 customers with AMI meters may have multiple customers that experience low voltage during a given day. Sometimes it is a distribution transformer issue, an undersized secondary, or maybe an old cable splice that was never documented. It could be several variables that caused the low voltage, but the meter data shows that the voltage on that feeder cannot be further reduced at that loading level and point in time. While this voltage data has been available to the utility for as long as it has had an AMI system, utilities typically do not investigate the data at the level of detail required for DVVC. The use of a DVVC algorithm and smart meter data can help identify and fix these weak points in the system. A DVVC system can help prioritize maintenance for the distribution system. These investigations take time and often require a lot of work to process and determine the validity of the data. The creation of algorithms to clean and normalize data is essential to the time-effectiveness of the solution.
In some cases, these challenges may lead to making more conservative assumptions, limiting the initial effectiveness of the algorithm. However, as data analytics tools continue to advance and as the algorithms become more sophisticated, greater visibility into the distribution system will be realized, which will allow utilities to react more quickly to address issues in the future.
As the system algorithm is implemented, updated and revised, the distribution system will be able to react dynamically. And as more equipment is monitored, it will continue to optimize. It is important to start this process as early as possible to meet the utility’s needs as the system grows and becomes smarter.
Existing high voltage can be another challenge for DVVC implementation. To date, much of the electric grid has operated on the higher end of the voltage profile. This was attributable to many reasons, whether to resolve low voltage issues downstream or to allow for more kilowatt-hours to be delivered to the customer. Today, however, the operational focus of utilities is shifting to energy efficiency. The problem is that if the voltage is too high at the distribution substation level — the starting point of the DVVC control — the algorithm will only be able to reduce the voltage a small amount using line devices. This will either push LTCs to their max taps (not able to reduce voltage any more) or it will open all the capacitors to address concerns about high voltage. Without all the capacitors as part of the algorithm, the system may not be able to meet the preset VAR and PF limits. Therefore, for maximum benefit, the whole system must be analyzed, rather than from the low side of the distribution substation down. Again, systemwide data analysis and studies can be used to determine ideal substation voltage, leading to a potentially more efficient and stable system.
There is a constant balance between voltage and VAR demands, creating the real benefit of a tried-and-tested volt/VAR control algorithm. There is a metaphorical “sweet spot” that DVVC can fine-tune and optimize on the system, but it will only optimize to the specifications created by the system engineers. As a result, utilities should conduct preliminary studies to understand the design and challenges of their system, and have the DVVC software vendor demonstrate the optimization to make sure it matches preferred solutions.
A lack of accurate system models is often the biggest obstacle to ideal grid operation. As modeling software, documentation and data management systems improve, creating accurate system models is becoming more feasible. Utilities across the country evaluate these new technologies. Distribution engineers are frequently asked, “Could we run a study or a simulation for DER (distributed energy resources) planning?” A common response: “It depends.” It typically depends on the availability of accurate network models for the system or how much work would be required to create or update them. As technology improves, it becomes more essential for utilities to create and maintain these accurate models.
Having these system models benefits all aspects of distribution system design, operation and maintenance. However, the lack of accurate circuit models remains a barrier. Engineers make conservative assumptions because of the general lack of faith in the data’s integrity. Now modern technology and data analytics, with accurate network models, will allow DVVC to push the grid into the next generation. Implementing a DVVC program is a great opportunity to dedicate resources to updating these models, as they will become integral in the future automated distribution management systems (ADMS) or distributed energy resource management systems (DERMS).