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Smart Grid Data Management: 7 Tips from the Trenches By Guest Editorial Jun 2, 2010 - 12:54:03 PM
By Frank Hoss
Many electric utilities are currently in the process of replacing their existing electro-mechanical meters or non-interval digital meters with interval, digital meters to support dynamic pricing and demand response and to enable energy consumption management by customers. What has been historically a single monthly energy consumption read per customer at 15 minutes intervals is now over 2,900 meter reads per customer per month. The amount of data is growing dramatically, and data management is no longer an option, but rather an important necessity.
Data management basics …
The following reflects seven important tips for Smart Grid data management identified by Accenture as a result of its experience with some 75 Smart Grid and AMI initiatives. Each of these tips has been assessed with regard to smart metering data.
1. Recognize Smart Grid data classes and their characteristics to develop comprehensive Smart Grid data management solutions
There are five distinct data classes:
· Operational – Represents the electrical behavior of the grid from intelligent network and smart meter devices
· Non-operational – Represents the condition, health and behavior of the assets
· Meter usage – Includes data on total power usage and demand values such as average, peak and time of day
· Event message – Consists of asynchronous event messages from Smart Grid devices, e.g., fault detection events
· Metadata – The overarching data needed to organize and interpret all the other data classes
It’s not really a question of which data classes might have the most value for utilities but rather where within the utility they are important. Because of the volume (tera-bytes, annually) and wide applicability of smart meter data, it supports having its own data class – meter usage. The principal use of smart meter data is to support the utility’s meter-to-cash process, i.e., creating revenue for the company. As customers are being offered more incentives through dynamic pricing (e.g., time-of-use rates, critical peak pricing, reversed inclining block rates), the energy consumption interval data and resulting aggregation must support complex billing.
Note: Other smart meter data available and its use (e.g., voltage), are discussed in some of the subsequent tips.
2. Consider how any data source can support multiple outcomes via analytics to get best value from the sensing infrastructure
3. Consider distributed data and analytics architectures to solve latency and robustness issues
For example, some AMI networks have distributed intelligence inherent within their design. Some collectors or concentrators will automatically do a power status check of a meter after having received a last gasp message to ensure that the last gasp was not the result of a recloser operation. In this instance the analytics is in the collector/concentrator and unnecessary outage messages are prevented from moving up through the network and Smart Grid technology (architecture) infrastructure to the OMS.
Latency and robustness issues need to be balanced with the capabilities of the network communications architecture. In addressing this, the critical communication requirements for intelligent devices (e.g., smart meters) are frequency and latency. Each intelligent device/business data requirement must be assessed.
It’s likely that more than one of the frequency or permissible latency categories is applicable when considering the data usage. For example, smart meter energy consumption data is typically needed hourly for residential customers and more frequently for C&I to support dynamic pricing and customer energy consumption management, daily as an aggregation and presentment to the customer for energy consumption management, and on-demand or near real-time when a customer has called a customer care representative with a specific question.
As meter data is generally stored within the meter, collectors and/or headends, permissible latency is typically not critical as it is transmitted to the back office typically daily (i.e., specified window of time). There are certain instances, however, when the timing/receipt of this data is more important. For example, if a customer calls in regard to a billing complaint and the CSR needs to query the meter, it’s unacceptable to have to wait several minutes for the result. Similarly, if voltage presence at the meter is being used to verify electric outage restoration activities, the results aren’t needed real-time, but likely within a reasonable return when requested.
4. Consider the holistic Smart Grid challenge when planning data management solutions (not just AMI) to avoid stranded investments or capability impediment
Because of the potential volume of data from Smart Grid solutions, the value of each data element in contributing to supporting the utility’s business and how it is going to be shared and made available across the enterprise must be considered to avoid stranded investments or capability impediment.
Holistically, consider the Smart Grid challenge when planning data management. For example, if smart meter “last gasp” messages/voltage presence are being used as input for the utility’s OMS, realize that when a major storm resulting in tens to hundreds of thousands of customers being without power, neither the OMS nor operators/dispatchers process this much data at once without the support of associated analytics and visualization capabilities. Some AMI head-end systems handle this volume of data more effectively than others so a clear understanding of the capabilities of the AMI head-end software packages is key to the overall architectural design.
It has also been seen that data from smart meters support the meter-to-cash process and customer energy management through energy consumption data; revenue protection through meter tampering events; outage detection, optimal field crew deployment and restoration verification through “last gasp” messages/voltage presence, electric network reliability (voltage control) through voltage data, etc. This single intelligent device provides data that impacts almost every major business/organization within the utility.
5. Design data architecture to match data classes and analytics/applications characteristics – a giant data warehouse is not the answer
With the deployment of interval, digital smart meters resulting in vastly more volumes of data, and the many uses identified for this data, very specific use cases have been developed. The result has been the development of an application, meter data management system (MDMS), for the meter usage data class. In addition to the analytics available in the MDMS, additional applications such as revenue protection and complex billing have been built to operate on this (combined with other intelligent network data) as well.
The use cases with a well-considered architectural design allow applications to perform capabilities they were designed for without the need for high levels of customization or a “giant” data warehouse, which both can negatively affect system performance.
6. Look to new tools like complex event processors (CEP) to handle new classes of data processing problems
The CEP analyzes event data in real-time to generate immediate insight and enable instant response to changing conditions, i.e., providing continuous intelligence versus responding to a singular event. What is meant by event data? An event is something that happens. Events are happening all around us all the time. Meter tampering is an event. There are also so-called “non-events” to consider, i.e., something that does not happen, but was supposed to. A non-event could be a scheduled interval meter read not happening. For the sake of simplicity though, all of these are referred to as events.
Complex event processing is a specialization within the broader field of event processing (EP). Simple event processing is the processing of a single event in isolation. Traditional technologies—transaction processing, messaging middleware, etc. -- are available to process individual events as they occur.
Complex event processing considers events in the context of other events rather than in isolation and has been utilized within other industries for years. But for many utilities it is an emerging concept to help manage the expected Smart Grid data influx. Complex event processing generates new events that are “high level events” —you could think of them as compound events— on the basis of business logic that is applied to incoming event data, in real-time. The result is insight and response to new information as it arrives. CEP looks for relationships among events, many of them asynchronous. For example, with smart metering, customers are being given many more options (time-of-use, critical peak pricing, direct load control, etc.) in the management of their energy consumption. As customers can sign up for more than one program, some programs may complement each other while others may produce conflicting results. CEP can be used to evaluate this data to determine how successful these programs are. It can also help to understand the interactions between programs and ultimately help to reshape these programs so that both the customers’ and utility’s goals are achieved.
7. Develop business process transformation plans at the same time as Smart Grid designs
Data requirements that fulfill business processes must be known. As the utility evolves based on the changing environment, in addition to leveraging technology to support the business, the process itself along with the people and organization likely must transform as well. Implementing technology without the accompanying business processes, people and organizational changes will degenerate benefits that could be fully realized – and may ultimately result in failure.
For example, with interval energy consumption data available from the smart meters and the resulting new dynamic pricing models, the billing business process must be capable of handling complex billing. Customers will want more control over their energy consumption choices. Making this information available through a customer portal can radically change the customer care interaction dynamics.
Frank Hoss is an Accenture senior manager who for the last five years has focused on the development of advanced metering and distribution Smart Grid solutions.
Additional SGN resources …
Why Your Grid is Already Smart (and How to Unlock its Data Treasure)
A Repeatable Framework for Achieving High Performance in Smart Grid Data Management (pdf)
Video and Slides from the 'High Performance in Data Management' Webinar
DOE Raises Bar for Smart Grid /Smart Metering Initiatives – Key Strategies for Success (white paper/pdf)
Powel Meter Management (video)
Smart Grid in Motion (white paper/pdf)
Landis+Gyr, in cooperation with SAP, brings utilities closer to an interoperable Smart Grid (press release/pdf)
AMI and MDMS Deployment Best Practices (white paper/pdf)
Bridging the Gap Between AMI and CIS (white paper/pdf)
The Role of Load Research in Automated Meter Infrastructure/Meter Data Management Initiatives (white paper/pdf)
Passive Revenue Protection Yields Results for a Major U.S. Utility (white paper/pdf)
.
Elsewhere on the Web …
CIO Master: New Epiphany: Smart Grids Require Real-Time all-IP Networks HP: Optimizing Meter Data Management Power & Energy: Smart Grid - Energy Efficiency Requires Data Efficiency
InformationWeek: Tibco Aims For Nanosecond Messaging
Progress Software: Enterprise Integration Guide: 8 Critical Considerations (white paper)
Electric Light & Power: Meter Data Management, Advanced Pricing Programs
Power & Energy: Utility Infrastructure Advances - Interview with WACS’ Chief Executive Craig Mataczynski
Itron: Meter Data Management – A Key to the Utility of the Future (white paper)
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