Distributed intelligence – the distribution of analysis, decisions and action away from a central control point – is not a new concept. From smart phones running mobile apps, to supply chain management solutions, to multiplayer online gaming, distributed intelligence and computing has proven to be a consistently effective approach to managing large, complex, data-intensive systems and organizations. And the Internet of Things is only accelerating this trend.
So, why is this trend so relevant now for electric utilities? This article will describe and analyze the three main drivers that are accelerating the use of distributed intelligence in electricity distribution network operations and consumer engagement, the principal use cases, and why utilities should take notice. The three drivers, in brief:
Grid Stability. The traditional utility model has been centralized on monitoring and control, but something big has changed. Since the dawn of electric utilities, load has been the key variable, and utilities have controlled generation and power flow to respond to changing load. In fact, control is the “C” is SCADA. With the emergence of distributed generation, all three of these are now variables – load, generation and power flow – not controlled by the utility. In this new world, the centralized control model simply does not work because there’s nothing to centrally control. Instead, the distribution grid must become the Intelligent Grid – a living, breathing organism, capable of analyzing local conditions and responding in the right place at the right time, within centrally established guidelines. This simple fact is one the principal drivers behind distributed intelligence in the utility space, manifested in many distributed intelligence apps such as location awareness, active transformer load management, active voltage management and others.
Consumer Transformation. There are actually two drivers here for customer transformation. The first is that utility consumers expect more. They expect instant notification, proactive energy savings and environmental programs, and a continually evolving pallet of service offerings. They are not being unrealistic or unreasonable since they already experience that same level of service in most of their vendor relationships. Distributed intelligence applications such as local load disaggregation-based Energy Audit, Active Demand Response and Appliance Health Monitoring help fill this gap in expectation. Apps such as the Distributed Outage Identification and Location application enable more rapid, accurate customer communication when service is lost and more rapid power restoration.
The second consumer transformation driver is the impending competition from non-traditional players. As giant tech companies and communication network companies invest in the energy management space, it has become clear that their goal is to step between the utility and the customer and own the local transactions that will occur in the distributed grid. Their goal is to be the first point of contact to the customers with local-market-capable technologies. Utilities can address this competition with advanced distributed intelligence applications. For example, applications such as the Real-Time Markets, which ensures that utilities have the infrastructure and capabilities in place to manage real-time local transactions as those markets open and maintain their financial position in those transactions.
Operational Efficiency: There are many opportunities for grid operation efficiency that have existed since the dawn of utilities, but the costs of achievement have traditionally exceeded the benefits. In many cases, these improvements, particularly in the low voltage network, could have theoretically been achieved with deployment of SCADA to every home. Unfortunately, the payback would have been measured in centuries, not years. Now, with the cost of distributed intelligence at the edge available for the same price as traditional “smart” meters, these efficiency use cases have become practical to implement. These are the more ordinary use cases, such as High Impedance Detection, Broken Neutral Detection, Theft Detection and Transformer Overload Identification, but they often drive utility distributed intelligence business cases as they provide savings against real OpEx expended today by every utility.
Edge processing has become a hot topic, and with distributed intelligence, utilities and cities can realize numerous benefits from the intelligent grid.
Author: Tim Driscoll is director of Information Management Outcomes at Itron, Inc. Tim is a pioneer in the distributed intelligence field, demonstrating the utility industry’s first meter based distributed intelligence applications in 2015, the utility industry’s first field deployment of DI applications in 2018 and overseeing the utilization of the “app” model concept for deployment of distributed intelligence applications to utility meters in general. Tim is an author of multiple patents associated with distributed intelligence field. Tim has been in the utility industry for more than 30 years, 20 of which he spent at Itron. Prior to his time at Itron, Tim worked as a utility distribution engineer and as a utility load researcher and forecaster prior. Tim holds a bachelor’s degree in mathematics and a bachelor’s degree in electrical engineering, both from Dalhousie University in Canada, and he is a registered professional engineer.
