Achieving high performance with smart meter data management systems. A leading practice approach for utilities

Transcription

1 Achieving high performance with smart meter data systems A leading practice approach for utilities

2 Contents Meter data systems: a critical enabler in smart grid implementations Core elements The five-level MDMS capability assessment framework Level 1: Commercial and industrial (C&I) meter-to-cash processes Level 2: Residential meter-to-cash, customer choice, conservation and demand-reduction rates Level 3: Smart metering data collection, field services and customer service processes Level 4: Smart metering operations and infrastructure Level 5: Smart grid enablement Why Accenture?

3 Meter data systems: a critical enabler in smart grid implementations Across the global electricity market, a growing number of utilities are implementing and leveraging smart metering technology. In many cases, the planned timelines for rollout are highly aggressive; corresponding with the pace dictated by government regulations and emissions targets. One of the primary consequences of implementing smart metering technology is that it results in a flow of data several magnitudes greater than any previous traditional metering schemes. This increased data volume will not only flow into the managing utility, but may also be passed to and from third-party retailers for processing under new and modified market transactions. The need to manage this data, and subsequently transform it into actionable business intelligence, creates challenges for utilities implementing smart metering. To meet these challenges, meter data systems (MDMS) provide utilities with a business-critical solution for storing, validating, aggregating and processing large volumes of data, in preparation for billing, settlements and other reporting and reconciliation obligations. In some markets, there will also be requirements for timely delivery of aggregated data to the market, and an MDMS can help utilities to meet these as well. 3

4 Core elements The specific architecture of an MDMS depends on the software environment provided by the vendor and the characteristics of each utility s unique deployment. However, at its core, an MDMS consists of several elements that are common to all such systems and are designed to facilitate predefined functions. These functions include: A centralized data repository for meter readings. Adapters to collection systems that enable raw data collected from smart meters to be loaded into the MDMS, while also enabling controls to be performed. Meter read components to validate, estimate, edit (VEE) and apply utility-specific or regulationspecific business logic to meter readings. An engine to calculate energy usage, demand and other bill determinants. Adapters to link in to downstream systems that consume processed meter data, such as billing, settlements, load forecasting, asset and customer Web portals. Figure 1 illustrates one example of MDMS architecture, including its touch points with upstream collection systems and downstream enterprise systems. Figure 1. Sample MDMS architecture, with upstream and downstream linkages. DR/DG control ESB/Web services SCADA/ EMS Work flow Meter Meter Residential AMI ADCS/ NMS/head-end Other AMI AMI ADCS/NMS/ head-end Manual adjustments Meter data Meter read retrieval Validate, edit, estimate (VEE) Usage calculation Meter events Reporting Calculate billing determinants OMS/DMS Customer information system Asset GIS Meter Meter Power quality metering system C&I interval data collection system ESB/Web services Profiling Forecasting Web presentment Meter data database ESB/Web services Load Weather data Energy vision ADCS = AMI data collection system; AMI = Advanced metering infrastructure; C&I = Commercial and industrial; DR = Demand response; DG = Distributed generation; DMS = Distribution system; EMS = Energy system; ESB = Enterprise service bus; NMS = Network system; OMS = Outage system; SCADA = Supervisory control and data acquisition. 4

5 The five-level MDMS capability assessment framework To help utilities gain optimal returns from their smart meter investments and help them achieve high performance, Accenture has developed an MDMS capabilities assessment framework, illustrated in Figure 2. The framework, which consists of a five-level approach toward the assessment of performance capabilities, can help utilities develop an optimized view of the benefits of implementing MDMS, in conjunction with the technical and functional footprint of their customer information system (CIS), smart meter and smart grid modernization initiatives. To facilitate a structured approach toward the assessment of MDMS capabilities, Accenture has aligned the framework along the dimensions of technical and functional scope/ sophistication. The two dimensions, however, are intended to be neither prescriptive nor interdependent specific implementations may reach the higher levels of maturity on a single dimension based on their level of sophistication on that axis and the business drivers they address. In general, the maturity level related to an MDMS implementation should be aligned to the implementation stage of the overall advanced metering infrastructure (AMI) deployment program. As Figure 2 illustrates, the five levels of the framework encompass specific functional scope of smart metering operations, along with corresponding technical maturity. A drill-down provides a more detailed look at each level: Level 1: Commercial and industrial (C&I) meter-to-cash processes. Level 2: Residential meter-to-cash, customer choice, conservation and demand-reduction rates. Level 3: Smart metering data collection, field services and customer service processes. Level 4: Smart metering operations and infrastructure. Level 5: Smart grid enablement. Figure 2. MDMS capabilities assessment framework. Smart grid data, thirdparty application development framework. CIMinspired models, open interfaces 5 Bi-directional SMI integration and control/process automation, CIMbased interfaces 3 4 Technical scope SMI collection aggregation, and service-order integration 2 Large-scale interval data 1 C&I interval data C&I meter-tocash processes Residential meterto-cash, customer choice, conservation and demandreduction rates Smart metering collection, field and customer service processes Smart metering operations and infrastructure Smart grid enablement Functional scope Notes: CIM = Common information model; SMI = Smart metering initiative; C&I = Commercial and industrial. 5

6 Level 1: Commercial and industrial (C&I) meterto-cash processes For several years, utilities have been running MDMS-like applications such as MV-90 PBS and the Lodestar Billing and Settlement applications. Historically, the reasons for using these applications have included enabling meter-to-cash processes for large customers, forecasting load schedules for service regions and performing settlement functions for transactions in the wholesale market. However, there are challenges in using existing applications in these areas. One is that tariff and bill calculations involve significant overhead in terms of cost and effort, requiring back-office of multiple (between two and four) channels of highly granular interval usage and demand data. This data includes collection, storage, VEE and calculation of complex billing determinants. At the same time, utilities using existing applications for MDMS-type functions may find they encounter further challenges from the introduction of new rates, regulatory changes that extend the footprint of interval-metered customers, and/or limitations in their existing systems. To date, MDMS have largely been used to manage low volumes of large customers with interval meters and complex rates. AMI brings a very high volume of interval data, with different rates and regulations, and the existing MDMS is unlikely to be sufficiently scalable or flexible to handle this change. For all these reasons, utilities may choose to deploy a specific MDMS for the relevant activities. Case study on the benefits of MDMS: Ontario local distribution companies (LDCs) and the Ontario Smart Metering Initiative (SMI) program 1 Under the Ontario Smart Metering Initiative Program, the central/ provincial MDMS operated by the independent electricity system operator enables consistent application of validation, estimation and editing (VEE) rules and bill determinant calculations for the Ontario Regulated Price Plan a three-tier, time-of-use (TOU) tariff mandated by the Ontario Energy Board. 2 Currently, the operational scope of the provincial MDMS is limited to residential and small business customers with demand below 50 kilowatts (kw). These customers are metered on a single-channel, interval-usage basis typically hourly. As a result, while utilities may deploy smart meters to all their new and existing customers (including general service above 50 kw and large C&I), the processing, data and complex bill determinant calculation functions for multiple channels of 15-minute or more granular interval readings will not be supported by the independent electricity system operator s meter data and repository (MDM/R) for some time to come. As this situation demonstrates, LDCs (utilities) facing infrastructure limitations in their ability to process interval data and calculate bill determinants for their largest C&I customer base can benefit from deploying a specific MDMS to support these functions. 6

7 Level 2: Residential meter-to-cash, customer choice, conservation and demand-reduction rates One of the most compelling reasons for implementing an MDMS to support a smart metering infrastructure program is its ability to potentially apply interval-metering processes to large volumes of residential readings. For example, this capability enables residential time-of-use, critical-peakpricing (CPP) and other conservation rates. The same capability enables massive or selective load profile data as well as the meter-tocash processes in Europe. Some of the specific considerations that lead utilities to adopt an MDMS include: Separation of concerns An MDMS provides separation of concerns in a utility s applications architecture. The MDMS can handle all the responsibilities related to meter data and can serve as a one-stop point for all current and historical meter usage information, thereby establishing consistent processes for publishing meter data to users of the information within the enterprise. Validation, estimation and editing The VEE component uses configurable, rules-based algorithms to validate meter data. It provides either actual meter data or the best possible estimate. Invalid data can be analyzed further to identify the root causes of any problems, thus enabling standardized representation, consistency and quality of the data related to a given class of consumers. Customer service MDMS can help utilities to engage their customers, respond accurately to billing inquires, enhance customer satisfaction and pave the way for higher rates of customer retention. It can also enable utilities to educate customers about their patterns of energy use, associated costs and environmental impacts, and can help encourage a consumer culture of energy efficiency and conservation. Customer web portals are commonly used to provide customers with aggregate energy usage data, detailed interval information and bill-to-date information. Calculation of bill determinants MDMS applications often include the ability to calculate bill determinants for flat, time-of-use and critical-peakpricing/critical-peak-rebate (CPP/ CPR) rates. Emerging trends Emerging trends, such as demand response and distributed generation, introduce potential complexities in meter data and billing that may expand the capabilities required from MDMS implementations. For instance, the need to support residential demand-response programs may require the ability to evaluate customer participation using: Demand-response event information. Customer override of load control reported by in-home devices. Customer baseline calculations using sophisticated methodologies that compare a number of similar nonevent days adjusted for weather. Comparisons to the baseline. Distributed generation programs will also require additional capabilities. Allowing homes, farms and businesses to generate their own power from renewable sources, (such as wind, water, solar and agricultural biomass) and distributing any excess electricity back to the grid for credit will require: The ability to meter and store at least two channels of energy interval data (import and export values) for all customers. Net metering (consumer is billed for net energy use during the various tiers). Sophisticated validation and estimation routines that account for energy imports from customers (and can accommodate negative net energy usage in an interval). Association of generation pricing tariffs to customer accounts. The ability to perform net settlement functions (whereby the consumer is compensated for energy delivered onto the grid using a separate generation tariff). Utilities whose business drivers include billing, customer service and efficacy analysis for their demand-response and distributed generation programs might also consider an appropriate MDMS implementation to provide these benefits. 7

8 Level 3: Smart metering data collection, field services and customer service processes Smart metering infrastructure data operational data store AMI data gives the utility information to unlock greater value. This information is only available and usable, however, if the utility has a fully functional and accessible data store. This requirement has been a key driver behind many MDMS implementations as part of smart metering initiatives. The information provided covers not only interval energy usage, but also status, events and alarms. Even utilities in regions with a provincial or central MDMS deployment (e.g., a single MDMS implementation for the entire regulatory jurisdiction such as a state, province or country that provides a common data repository, bill determinant calculations and customer usage presentment for all utilities and customers in the jurisdiction) have benefited from the operational visibility and efficiencies achieved from their own smart metering infrastructure data. One such benefit is the ability to monitor and act on health and tamper information reported by smart meters. Enabling such functionality for all utilities would typically be beyond the scope of a central MDMS implementation. Enabling field service processes using smart metering infrastructure Building greater efficiencies into field service processes is the logical next step to implementing an operational data store. Examples of the efficiency gains available include integration with service-order systems to automate service order creation and field dispatch for instance, to investigate a low-battery warning reported by a smart meter and the ability to optimize processes when customers move premises (using the latest readings in the operational data store/mdms for initial/final reads). Smart metering collection aggregation In addition to managing energy metering data for smart meters, an MDMS can also provide a collection of adapters and interfaces to integrate multiple smart metering infrastructure systems that use different technologies and data formats. This feature effectively decouples downstream applications from automated metering infrastructure, allowing the integration of new technologies as they emerge, and the decommissioning of old technologies, without being restricted to a single vendor or AMI implementation. This means consumers of AMI data outputs can use the MDMS as a single, consistent interface across the various AMI systems, with data presented in a standardized manner. For instance, in Canada, one utility has adopted an operational data store/ MDMS implementation to ensure that any data, prior to being reported to the provincial or central MDMS has been: Validated using the rules specified by the independent electricity system operator VEE specifications. Converted to the independent electricity system operator meter data format. Standardized for content (e.g., time representation and engineering units). This use of an MDMS as an operational data store for collection aggregation has helped the utility achieve tighter control over the quality and consistency of data reported by various vendor systems. Furthermore, the early identification of data quality issues (such as high numbers of missing reads or other anomalies) has enabled the utility to attempt remediation before the data is reported to the independent electricity system operator and is presented to customers. Collection Collection refers to the ability of an MDMS to provide sophisticated capabilities such as read arbitration, read integrity inspection, data rejection, data aggregation, scheduling and service level agreement (SLA) monitoring across multiple smart metering infrastructure head-ends and systems. This is a key benefit of MDMS implementations in terms of technical scoping, particularly where utilities expect to deploy multiple smart meter systems to cover their territory. 8

9 Level 4: Smart metering operations and infrastructure Two-way process automation In the context of bi-directional smart metering infrastructure networks, the MDMS often acts as the routing and component for implementing the required two-way processes. A common implementation of this ability often includes the integration of turn-on/turn-off processes at a utility using a combination of manual processes and smart meters with an integrated remote connectdisconnect (RCD) switch. In this case, once a customer information system determines that customer power is to be turned off; an MDMS may be implemented to determine, depending on the meter type, whether the turn-on/turn-off requires a field service order, or can be executed directly through the smart metering infrastructure systems. In the case of multiple smart metering infrastructure systems, the MDMS also plays the role of abstracting the business process from specific implementation details; for example, where the smart metering infrastructure system requires that the meter be read following the operation of the RCD switch to verify RCD state. Other examples of process automation enabled by the MDMS include: On-demand reads initiated by customer service. Outage pings. Smart meter configuration and firmware upgrade. Demand-response event orchestration and. Exception monitoring, reporting and The MDMS can subscribe to events, status messages, alarms and alerts from automated metering infrastructure to provide real-time monitoring of the network and field devices. The information provided can generate insight into operational issues, the health of devices and analysis of operational trends. Examples include: Use of reported meter health events to dispatch meter technicians to the field and review trends that may indicate quality issues with a particular batch or type of meter. Detection of tamper and theft from unexpected tilt indicators. Analysis of momentary outage indicators reported by meters on a distribution feeder or secondary to identify the need for vegetation trimming. Integration with intrusion detection systems to notify a potential security breach in the smart metering infrastructure network (such as unauthorized access at the meter s optical probe). Calculation and reporting of reliability indices from smart meter outage and restoration information. Standards-based interfaces Given the tight integration and dependence on process automation to facilitate many of the level 4 capabilities, a standards-compliant MDMS with, for example, IEC 61968/ CIM or Multispeak interfaces to enterprise systems providing abstraction from vendor specificity can be a key component of the overall enterprise architecture, helping to ensure flexibility and extensibility through the life of the program. 9

10 Level 5: Smart grid enablement As utilities strive to optimize their operations by deploying new smart grid technologies, the MDMS can become a vital component in enabling their smart grid journey. A particular driver in enabling the MDMS to play this role is the set of enhanced capabilities around data collection, storage, analysis and decision support made possible by smart devices on the grid. There are several business drivers that may cause a utility to consider implementing a smart grid-enabling MDMS in support of a smart grid data solution. These include: Integration of distributed generation While small amounts of distributed generation will not have a major impact on the distribution grid, some developments may result in widespread propagation and/or concentration of distributed generation on the distribution network. These include programs allowing homes, farms and businesses to generate their own power from renewable sources wind, water, solar power, agricultural biomass and send excess electricity back to the grid for credit, and the eventual mass adoption of plug-in electric vehicles that can act as distributed generation resources during peak periods. These diverse distributed generation resources typically use inverter-based technologies. Large concentrations, defined by some industry studies 3,4 as more than 10 percent of serviced premises on a feeder, or propagation of distributed generation on rural, low-density feeders, can result in a variety of problems around power quality, including over-voltage, under-voltage, phase voltage imbalance, sudden voltage changes, excessive harmonics, frequency fluctuations and unintended-islanding. While sound electricity network design can reduce or eliminate most of these issues, the distribution company will still have to maintain the visibility of distributed generation sources on the grid, while also monitoring the distribution network constantly for voltage, power-quality, frequency and other instrumentation. The MDMS can store metering data related to distributed generation, including load profiles, inactivity periods and quality of supply data. This kind of information can be used by other external systems to compute data analysis and correlation, providing valuable inputs for the improvement of grid operations. Integration of demand response Integrating demand response as a resource will require the ability to coordinate the communication of demand-response events, monitoring, measurement and verification functions. In addition, it may also demand further capabilities, such as: Predictive analysis of the expected participation levels on a feeder to support distribution operations by temporarily switching a subset of customers to another feeder during a planned outage or to manage the peak load expected on the basis of a weather forecast. Predictive analysis of the capacity available for each demand response program to support the utility s ability to bid demand-response as a resource in wholesale markets for capacity, energy and ancillary services. Smart grid device and configuration A growing number of utilities are examining how the smart meter network can act as the communications network required to create and implement a smarter distribution grid. New devices, such as transformer and feeder meters, are becoming integral elements of smart grid deployments. Utilities may also need to track in-home devices, such as thermostats and load control switches which may not be the utility s own assets and their life cycles, as part of device and configuration. Furthermore, many of these new devices are expected to be capable of remote configuration and reprogramming. These requirements introduce significant new operational challenges in how utilities manage the configuration and life cycle of these assets. The deployment of grid monitoring equipment, such as transformer meters and feeder meters, will also require utilities to maintain accurate information about the distribution network hierarchy. For instance, utilities will need application support to keep track of transformer meters and their serviced end-points. Neither of the capabilities highlighted above is part of traditional asset systems. However, an MDMS working in conjunction with a device configuration/firmware system can help utilities to bridge the gap between their as-is and to-be states. 10

11 Advanced asset Advanced asset is the ability to manage the operational state and performance of assets on the distribution network. By combining information about the distribution network topology with data from new smart-grid devices such as transformer meters, low-voltage and mediumvoltage sensors (feeder meters) and metered data from smart meters and grid sensors utilities can develop a wide array of monitoring, analytical and visualization applications. In combination, these applications provide the distribution control center with a much higher degree of situational awareness. Distribution system planning groups can also use the same information to achieve a number of benefits. These include understanding the operational characteristics (such as loading, losses, phase imbalance and utilization) of the distribution network assets, optimizing the utilization of existing assets and the ability to defer capital expenditure for new assets. An MDMS with the ability to track grid assets, network hierarchy and data reported by grid devices should be a further integral part of smart grid architectures, and of the overall solution for advanced asset applications. Extensibility In the context of smart grid enterprise architecture, the MDMS may not be the only solution for implementing all smart grid analytical applications. What is important, however, is that the MDMS performs an enabling role for these functions. From a technical perspective, this role implies that the MDMS needs to be an open system, with common information model (CIM)-inspired data models and standards-compliant, well-defined interfaces to which third-party application program developers can easily interconnect. Why Accenture? Accenture is already applying its industry-leading expertise to help many major utilities worldwide identify the benefits of major investments in assets such as CIS, smart meters, smart grids and enterprise asset (EAM), and then articulate those benefits in a robust business case. As we undertake these projects, we are differentiated by our holistic understanding of the business benefits that utilities can achieve from the combination of customer information systems, MDM/R, and smart meter/ grid infrastructures. Accenture s meter data system (MDMS) capabilities assessment framework is a demonstrated methodology to help utilities evaluate the technical and functional impacts of their CIS, smart meter, and smart grid modernization initiatives, and assess, in detail, the potential benefits that can be achieved through implementation of an MDMS. While no single commercial MDMS supports all of the capabilities that we describe in this paper, the potential benefits are wide-ranging even for those utilities and local distributors in provinces with centralized MDM/R implementations. To find out more about how our framework could help your utility on its journey to achieve high performance, please contact: North America Dileep.Rudran [email protected] Asia Pacific David Lester [email protected] Europe, Africa and Latin America Richard Hanks [email protected] 11

12 About the Accenture Utilities group The Accenture Utilities group has more than 30 years of experience working with electric, gas and water utilities worldwide. We work with 93 percent of the utilities on the 2010 Global Fortune 500 list, providing the deep industry knowledge, people and assets utilities need to develop the strategies and adopt solutions to improve performance in the dynamic energy market. With 100 smart grid projects in more than 20 countries, one of Accenture s key focus areas is in helping our utilities clients with the transformation to a smarter grid. From generation to in-home energy, from strategic blueprints to operational data analytics, and from the boardroom to the operations center, Accenture offers the skills and experience that can help utilities frame their vision of a smarter grid and then achieve its many benefits. About Accenture Accenture is a global consulting, technology services and outsourcing company, with approximately 211,000 people serving clients in more than 120 countries. Combining unparalleled experience, comprehensive capabilities across all industries and business functions, and extensive research on the world s most successful companies, Accenture collaborates with clients to help them become high-performance businesses and governments. The company generated net revenues of US$21.6 billion for the fiscal year ended Aug. 31, Its home page is Copyright 2011 Accenture All rights reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture. References 1 Ontario, Ministry of Energy, Smart Meters, electricity/?page=smart-meters. 2 Ontario Ministry of Energy, Smart Meters and Time of Use Pricing, conservation/smartmeters; The Independent Electricity System Operator (IESO) Smart Metering Entity, 3 Keller, J. and Krposki, B., Understanding Fault Characteristics of Inverter-Based Distributed Energy Resources, NREL Technical Report TP , January Baran, M.E. and El-Markaby, I. Fault Analysis on Distribution Feeders with DG, IEEE Transaction, WSS