Reaping Value from Smart Data: The Impact of Time
Widespread adoption of smart metering across the globe, particularly in the US, is propelling utility firms to unlock the real value of smart data. Next-generation meters provide utility firms a tidal wave of data; however, deriving insights and value from the data comes with its own challenges such as extracting information at the right time and applying it in the right business context. Leveraging smart data for multiple business purposes opens many opportunities, drives swifter enablement of smart metering and smart grids, allows utilities to adopt distributed energy resources, and improves operations and processes with better call to actions. Smart metering generates a gamut of transactional and operational data, at times with a reduced latency.
While granularity and variety are important aspects, depending on its age, optimizing smart data can lead to better asset planning, optimized energy delivery, reduced operational expenses, and improved customer satisfaction. While a common myth states that the value of the smart data increases significantly as latency reduces, it is not true. The value that data holds and its dependency on age largely depend on the use case under consideration. Data can be categorized as instantaneous, real-time, intermittent, and historical. Depending on this classification, we can draw up different use cases for each data life cycle stage.
Deriving value from smart data
On one end of the spectrum, in terms of the age of data, we have a wealth of information provided by historical granular data. Such data can be used to analyze and improve load forecasting over a span of time, enabling better asset planning and maintenance, optimized allocation of network devices and tools, and higher responsiveness to high peak load conditions.
At the other end, instantaneous data is available in the metering device on the spot and can be leveraged to monitor the health of smart meters and predict their life span. It can also be used to identify safety and hazard situations. For instance, data generated by a smart meter about voltage and current parameters at a particular instance can save us from fire hazards by enabling preventive actions. This in turn helps in identifying potential points of failure in the network and improves the life of devices. The two-pronged new generation of smart meters allows utilities to execute business analytics at the edge, thereby accelerating decision making at the edge itself. This is a sure-fire win and the foundation for the pioneering technology of distribution intelligence, where power and transaction flows are managed in real time, resolving issues faster, predicting and managing faults, and enabling restoration on the spot.
In between the two spectrums of the age and latency dimensions, based on the life cycle of the data, there are numerous use cases to understand consumer usage patterns, develop real-time billing tariffs, time-of-use (TOU) rates, demand response programs, etc., all of which fall under the traditional benefits of smart metering. To be able to derive real value from smart data and use it across different customer segments, data must be made available across business units, such as customer service, transmission, and generation.
As data ages, its use can become much varied using analytics. Another category of data whose analysis spans the entire data life cycle in terms of age is that used for research and development (R&D). Such data is used to develop different energy products for end-users and to define the case for new business models.
Business analytics paves the way for futuristic, customer-centric utilities
The age of the data provides different levels of insights and perspectives, which triggers a set of business benefits and paves the way for new business models. From a functional standpoint, the value of instantaneous data mostly lies in complementing enterprise systems with information to ensure grid stability, overall safety and customer benefits, operational gains, and asset maintenance. Real-time data, the next most widely available and used data, enables customer participation in energy saving programs and supports utilities in offering energy advisory services and offerings. Most utility firms rely on intermittent data to derive useful insights from data usage. Analyzing this data also helps consumers understand their usage patterns and become an active participant in the energy delivery process, rather than act as passive rate payers. The long-term benefits of granular data can be derived from mining the historical sea of data, which can be used for asset management, load forecasting, and other purposes.
Among these, addressing safety and customer concerns during an emergency are of paramount importance and are one of the most investigated use cases today, as utilities strive for the highest level of safety standards, grid reliability, and customer experience.