As utility customers invest more in advanced energy technologies, buildings are becoming dynamic energy hubs. Anywhere, anytime, a building could be a consumer, storage point, or generator of energy. And a building’s energy profile could change instantaneously as it responds to signals from internal controls, the grid operator, or third parties to deploy assets like controllable water heaters, smart equipment, solar, and storage.
The dynamic commercial building of the future will be a flexible source and user of energy, variably consuming electricity through a mosaic of connected energy systems – from the charging stations in the parking lot to the heating and cooling systems and appliances.
This is true not just of large and sophisticated buildings. Through the power of grid automation and smart devices, anyone with a grid-connected device, such as a hot water heater or refrigerator, will be a part of this dynamic network.
A pressing question for utilities, then, is “Do we really understand these new hubs and the customers that control them?” The answer to this question has big implications on everything from customer engagement to system planning.
Simplicity First: Engaging with Dynamic Energy Customers
Even for customers in dynamic buildings, the needs of building occupants will still come first. Comfort, safety, and reliable operations rank above other considerations, such as minimizing energy expenditures. But as experience with demand response (DR) and variable energy rate programs has shown, energy managers can balance additional economic priorities if the right price incentives are in place.
To manage the complexity of serving customers that are buyers, sellers, or traders or energy, utilities will need to deeply understand their customers and the buildings they operate. To engage customers with new programs or offers, utilities will need to leverage this understanding to effectively explain the bottom line impact for each customer – making it simple to understand how different actions could increase benefits or raise costs.
Simplicity must be the name of the game.
For example, a utility may want to promote heat pumps as part of an energy efficiency campaign. Customer intelligence, created from advanced data analytics, provides the contextual understanding to identify the customers that would benefit most from converting to a heat pump, while providing a forecast of how heating with a heat pump would increase that building’s electricity needs.
Once a utility engages target customers with this information, a customer may want to understand how a battery storage system would change the financial returns of his heat pump project. And perhaps it is worth considering a lighting retrofit project, too. What’s more, the energy profiles of his building will change as it consumes, produces, and stores energy in new ways.
One-by-one, the calculations may be manageable. But doing this at scale for every customer without using powerful analytics is neither feasible nor scalable.
Cloud computing platforms perform computations quickly so that utilities can generate intelligence across hundreds of thousands of customers instantly. Data-driven information provides the tools to manage buildings intelligently and engage customers productively. That way, utilities can use customer understanding to guide every customer to the right energy options.
Flexible Loads Demand
As building loads become much more flexible, utilities need to visualize them as dynamic nodes in the energy system. Understanding and forecasting customer demand previously relied on aggregate, long-term forecasts that were fairly stable from year to year. Weather and occupancy patterns primarily drove energy consumption.
Now, distributed energy management systems, algorithms, and automated controls drive energy consumption. Load profiles can change dramatically from minute to minute.
Understanding flexible capacity across a service territory is no easy feat. Data analysis provides the kind of large-scale, high-resolution intelligence that allows utilities to understand and forecast the energy needs of every building and translate that information into advice and solutions for customers.
For example, FirstFuel data analysis of load profiles of 1,400 buildings reveals clear differences in how much a given building’s load fluctuates over the course of a year. The load in some buildings swings sharply from hour to hour, while in other buildings, loads barely change at all. Understanding load flexibility will be key to managing dynamic buildings. For example:
Reality Check – How Utilities Can Beat the Trend
Currently, some industries, such as retail, are experts at gaining deep insights into their customers, and using those insights to create and provide better user experiences. Utilities will need to master this skill, too, in order to help current customers, and serve the business customers of the future.
Currently, business customers optimize energy mainly for building performance and to provide comfort. They are aware of expanding energy options, but still see utility offerings in siloes. Soon, business customers will optimize even further for price, which will be based on grid impacts, and will face choices among many service providers. As more service providers begin courting prospects, customers will expect guidance on how products and services fit together. In the future, utilities will work with the customer to show different options, provide guidance and unite offerings into one value proposition.
As buildings become energy hubs, utilities will need to understand who their customers are and what they expect to deliver safe, reliable and cost-effective options.
Avots: renewable energy world
