IoT, Automation and Cloud Analytics Key for Utilities and DER
The electric utility industry is no stranger to the Internet of Things (IoT) and is, in fact, already solidly invested in it. The smart grid is made possible by the application of IoT technology such as two-way communications, smart sensors and data analytics to the grid infrastructure to improve reliability, increase efficiency, integrate more renewable energy and distributed energy resources (DER), and better engage and empower utility customers.
Utilities are now beginning to more thoroughly analyze smart device data to drive smarter decision-making, reduce costs and enable new services. More specifically, utilities are increasingly beginning to operationalize the sensor data being gathered to enable more efficient and proactive utility efforts, both in core reliability efforts and in the utility’s approach to edge-of-grid and other emerging new services.
IoT, DER and the Cloud
Decreasing technology costs and supportive government policies have played integral roles in the increased DER adoption by both residential and business utility customers. This includes adoption of rooftop solar photovoltaic (PV), wind turbines, electric vehicles (EVs), energy storage, energy management systems and whole-house diesel generators. While these resources are increasingly being viewed as helpful extensions to the current electric distribution grid, they also can negatively affect grid reliability and performance unless they are skillfully managed by utility operators.
The IoT, in particular, plays a vital role in all of this. As DERs are integrated into the electricity distribution grid, communication channels are needed to allow numerous different systems and devices to send commands based upon customer choices. At times, it will be necessary for the utility to communicate beyond the customer’s smart meter directly into the customer-side DER to ensure these choices are successfully executed. The IoT network will allow data and commands to flow among DER, utility operational systems and equipment, such as sensors and load control devices, to maintain reliability and meet peak electricity demand.
To do all of this in real- or near real-time, the marriage of automation and cloud-based analytics with IoT is imperative for the modern utility dealing with DER integration.
There are many ways in which utilities can leverage IoT and related technologies to successfully manage the challenges associated with incorporating customer-owned DER into the electric grid. There are three areas of key importance to electric utilities working to effectively integrate DER and grid-side resources: 1) minimizing asset risk; 2) increasing customer choice; and 3) alleviating utility constraint.
Minimizing asset risk
Utilities often run into challenges when integrating DER, as intermittency in distributed renewable generation and variations in consumption patterns create variabilities in energy supply. In addition, the intermittency of supply creates real risks to grid health and reliability, and can place additional strain on traditional distribution grid assets.
It’s difficult for utilities to predict and accurately forecast granular intermittent renewable generation, its aggregated impact on grid connection points, and overall energy supply. In addition, because solar energy and wind are not dispatchable, utility operators can’t choose when to use the electricity generated by these resources. Instead, they must take the electricity when it’s available and mitigate any impacts of its inherent intermittency.
By taking a data-centric approach to monitoring, control and operation of edge-of-grid needs, as well as traditional distribution grid needs, a utility can better manage operational asset risk and performance. This type of approach allows the utility to identify and manage negative performance patterns via cloud-based analysis and network modeling of DER sensor data.
How would this work? First, the utility must understand every distribution asset’s condition as well as its importance in the distribution mix. This means that when adding DER to the grid, it is important to be able to model the generation output profile of every DER, noting its location, condition of use and other unique attributes, and then incorporate that data into the broader distribution model. By aggregating all the distribution grid asset data into a single system and continually updating work history, condition rating and condition changes, the utility gains a more reliable view of asset health.
Cloud-based analysis and network modeling of DER sensor data make the process of identifying and managing negative performance patterns smoother. The cloud allows for multi-sourced and cross-sourced analytics, as well as historical, trending and comparative analytics, to happen quickly, and it provides scalability to meet the ever-growing needs of a more distributed grid. In addition, using the cloud to perform these functions lowers the need for a comparably larger IT department and enables reduced IT maintenance costs.
Increasing customer choice
More work is being done around the concept of a “plug-and-play” distribution grid for the future; one that would involve a transactive energy market that will allow consumers to participate right along with traditional generators. Already, utility customers are increasingly participating in demand response and load shifting programs, and additional opportunities are opening up for customer participation in the sale of excess and stored DER generation into other markets.
IoT is used as a mechanism for the utility to manage the actual control of the DER device based upon the utility’s contract with the DER customer or the program obligations to which the customer commits. With IoT, utilities can make the shift in real time as needed per the contractual agreement, instead of having to rely upon data after the fact to confirm that the customer has met the stipulations of the particular demand response or load shifting program to which he or she has subscribed. The use of IoT technologies allows the utility to model, manage and control an individual DER’s response in real time, unlocking the greater value—including improved investment performance and lower operational costs—to the utility along the way.
In the future, a transactive energy market will include both utility customers’ DER, as well as that of other third parties in a new market exchange equation, dynamically balancing energy supply and demand in a real-time market.
Alleviating utility constraint
Taking the concept of dynamically balancing energy supply and demand of DER one step further, the IoT can also alleviate utility constraint on peak load days without the utility having to shift to “peaker” generation plants, which are extremely costly to maintain year-round in order to operate them only when there is extremely high demand for electricity. By using demand response to leverage consumer DER assets with centralized distribution assets, the utility could dynamically shift output among the combined generation resources in real-time to manage peak load, instead.
Here, too, the cloud can be useful. If this type of extreme dynamic load management is cyclical, rather than necessary throughout the year, a cloud environment is, by its very nature, uniquely suited to the flexibility of scaling to peak demand as needed and then returning to a more regular operational level afterwards.
IoT is providing the glue
The addition of consumer-owned DER not only pushes the limits of the traditional utility distribution grid, it also provides new opportunities for grid owners and operators. Sensor data from assets along the distribution grid will continue to be key to ensuring end-to-end visibility, as well as the means to model, manage, analyze, control and optimize DER. IoT provides the glue connecting all the pieces of the new distribution platform, and the cloud provides the environment for doing it quickly, with reduced risk and lower costs.| PGI
Brian Bradford is vice president of asset solutions for the Utilities Global Business Unit of Oracle Corp. He is responsible for the profit and loss of utility operational software applications and delivery. He has more than 20 years experience in the utility space and joined Oracle from GE where he was general manager of hosted software and analytic solutions. Brian has an MBA from Harvard Business School and an undergraduate degree in finance from the Wharton School of the University of Pennsylvania.