by Dan Hokanson, Landis+Gyr
Long-standing electric utility business models are rapidly becoming outdated in light of new technologies, policy changes and more demanding consumers. Historically, there was little financial or operational value to the data generated by consumers because it was limited in scope and frequency of delivery to be of value to parties other than the electric provider’s own billing and operations departments.
The quantity, frequency and quality of data generated by consumers and its usefulness to utilities, however, grew exponentially as smart grid infrastructure was deployed. Some information is primarily operational, such as demand response, load profile flexibility and distributed power and storage for optimization of system performance and asset utilization. According to IBM’s Institute for Business Value, information on energy consumption patterns, other consumer demographic and behavioral information and access to personal connections and networks for marketing purposes, are the foundation for new revenue sources for companies that can effectively leverage the information. The question is: What can utilities do with all of this incoming data to recognize new revenue sources? Processes for using smart grid data to improve the meter-to-cash process are well-established. Where utilities can gain significant operational advantages is in applications beyond the billing determinant.
Why isn’t this happening more? Because utilities lack the appropriate enterprise solutions required to manage incoming data efficiently. Ideally, an enterprise-level system for smart grid data can:
- Consolidate data from all grid data sources in a centralized system-of-record repository;
- Standardize data for use in different back-office applications;
- Interconnect field grid data collection systems and a variety of enterprise applications;
- Enable incoming data for operational programs and deliver it accordingly; and
- Analyze usage patterns, events, system performance and programs.
As the central point of connection for the data collection points and systems in the smart grid, the MDMS should be able to handle all these tasks–if it has some essential components. Namely, a centralized data repository, a versatile MDMS application and the data exchange mechanisms that allow it to interface with back-office, meter reading and other systems through international and industry standards such as International Electro-technical Commission-Common Information Model 61968, SAP Meter Data Unification System, MultiSpeak and others.
With this kind of technology in place, utilities will find additional opportunities beyond meter-to-cash efficiency. Many utilities are already implementing these programs. In each case, utilities developed a list of measurable objectives, put together a budget and establish realistic expectations for return on investment.
This application helps utilities identify and collect unbilled revenue. The cause could be technical, such as a malfunctioning meter or a read that is taken but not delivered. Revenue assurance also addresses non-technical issues, such as diversion and tampering. Specifically, the MDMS can be used to detect issues earlier, accelerating resolution. For example, it can find patterns in comparisons of highs and lows in the billing cycle or daily usage. It can flag unexpected usage in inactive accounts, as well as excessive usage through spike and scalar checks. It can correlate a disconnect alert followed by several days of low or zero usage instances and send the appropriate “red flag” for investigation. In these cases, the MDMS allows utilities to identify suspect usage across the install base, correlating smart meter events with typical usage patterns and alarms.
Another example of revenue assurance is the ability to find transformer-level reads that are higher than actual meter reads for the area served by the transformer. Revenue leakage of this kind might be caused by mistakenly unmetered points, such as street or parking lot lighting. Even a flawless meter-to-cash process will not detect these situations, which means utilities are leaving a hefty chunk of revenue unbilled.
Power quality and outages
Utilities can expect high rates of return from using MDMS data in distribution operations. In this area of the utility, MDMS data enables utilities to optimize distribution through grid-wide and localized assessments of power quality, as well as automated command and control functionality.
In this scenario, the MDMS receives and filters notifications related to outages and restoration, allowing utilities to easily determine if outages are occurring at the transformer or the endpoint, as well as assess the full extent of outages across the distribution network.
Scouting and verifying restored endpoints without truck rolls can be a source of significant savings. One moderately-sized investor-owned utility, estimates savings of a million truck miles annually. The ability to detect post-restoration nested outages is also especially useful for restoring service faster and more efficiently. Better outage response makes a community more attractive for economic development.
According to an IBM Global Utility Consumer Survey, more than 50 percent of respondents believe that smart grid technologies will improve household energy awareness and control, lowering the total cost for household energy usage.
To deliver, utilities must provide consumers with current, valid and correlated usage data to create the pathways to engage consumers in changing their own usage consumption patterns. Sending alerts via email and text messages keep customers informed and aware during outages. Customer service representatives can use this near-real-time information to validate outage calls from customers, investigate high bill complaints and resolve other service issues much faster. Customers can also be enlisted to complete their own enrollment online. Other customers will seek out their own current and historical usage data to set goals and monitor conservation efforts.
Complex metering and billing
Conventional meter-to-cash processes often struggle with complex metering and billing scenarios that are becoming more common. Customers who generate their own power from solar panels or other sources will need to return power to the grid and earn the proper credit. Customers with electric vehicles may be charged a discount rate for vehicle charging. Billing can be complicated outside the single-family home, where large campuses have multiple metering and submetering points that create a matrix of direct and aggregated billing needs. This is yet another area where MDMS data can deliver value. The MDMS application should already know where all meter data originates, which makes it a helpful tool for ensuring proper billing and credit in these situations.
MDMS data can be used at the executive level to provide exceptional strategic visibility into the distribution network. Decision-makers can use MDMS data to justify capital expenditures, measure improvement in total cost of ownership in multiple lines of business and make sure utilities are meeting regulatory requirements. For example, the MDMS data is a source for more accurate asset management.
This comes from its ability to compare a transformer’s rating with the actual usage of the meters associated with it. Making this comparison can help identify under- and over-utilized transformers, enabling utilities to prioritize potential safety hazards, calculate how under- or overuse is affecting the transformer’s performance and life span, and make more informed decisions about asset repair or replacement.
Analytics is a big buzzword in utilities for good reason. Sophisticated analysis of smart grid data lays the foundation for enterprise-wide improvements in critical areas.
While the value of enterprise-level analytics is undisputed, there are challenges related to the scale and scope of implementation necessary to bring these analytics to life. For most utilities, the capital investment required to build customized data stores, rules engines and reporting functionality is out of reach.
A more accessible alternative is the MDMS, which is uniquely positioned to provide daily operational benefits through analytics embedded within the system.
The MDMS is located close to the original data sources across the smart grid, so utilities can expect low latency of receipt from the distribution network, as well as the ability to move data where it is needed on demand. The data available through the MDMS should be current and valid, making the MDMS a reliable source of data that can be analyzed to benefit many operational systems. MDMS can, with the right analytics capabilities, provide a readily available way for utilities to generate greater value from the data that is already being collected and systems that are already in place.
Many utilities might not think MDMS is a vehicle for analytics, but they should consider how much data the MDMS captures from every part of the smart grid. If MDMS can be fine-tuned to examine specific groups of users, rates and other special conditions, opportunities for operational insight are numerous.
These findings help utilities create a much more detailed picture of operational activity and set the stage for utilities to make meaningful improvements in performance, profitability, customer service and overall efficiency.
Daniel Hokanson is the director MDM Solutions, responsible for product strategy of the Gridstream MDMS and associated analytic solutions. Most recently, he managed a software business line at 3M in supply chain and performance management. He also serves on the board of directors for InsightFormation, a provider of srategy aligned management software and consulting services. Hokanson holds a bachelor’s degree in both engineering and computer science from Minnesota State University and an MBA from the New York Institute of Technology.
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