Unified AMI Information Models to Support Diversified Smart Grid Systems and Applications

Transcription

1 Unified AMI Information Models to Support Diversified Smart Grid Systems and Applications Zhao Li*, Jiang Zheng*, Fang Yang +, Aldo Dagnino* *Industrial Software Systems + Grid Automation ABB US Corporate Research Center 940 Main Campus Dr. Raleigh, NC, leezhao@gmail.com {Jiang.Zheng, Fang.Yang, Aldo.Dagnino}@us.abb.com Abstract There is a considerable number of power distribution utilities have installed advanced metering infrastructure (AMI). As a result, meter data management systems (MDMS) become a critical information gateway in the distribution network: it aggregates meter data measurements from various AMI systems using different information models and tailors the collected information to support various smart grid applications, which have their own special requirements on the meter information. Under this situation, defining a unified information model for MDMS to minimize the information gaps between AMIs and smart grid applications becomes necessary. This paper proposes such a unified information model based on the investigation of meter information standards (models) in the current market. Three example smart grid applications supported by the proposed unified information model are illustrated. I. INTRODUCTION The advanced metering infrastructure (AMI) typically consists of metering, communication, and data management systems, offering the two-way transportation of customer energy usage data and meter control signals between customers and utility control centers. AMI was originally developed from the automatic meter reading (AMR) system, the one-way communication infrastructure that automatically collects meter measurements from residential smart meters to utility control centers for monthly billing and related business activities. Being the next generation of AMR, AMI not only increases the frequency of collecting data from the monthly to real-time or near real-time but also adds the communication channel from the control center to smart meters, making it a two-way communication system. For most utilities, AMI is a communication network that can reach the residential customers, transmitting a large amount of data from the field devices to the control center in real-time or near real-time (e.g., every 15 minutes). In the past several years, the deployment of AMI technologies has been boosted by the U.S. government s economic stimulus plan and the Energy Policy Act of 2005 (EPAct 2005) [1] that requires electric utilities with annual sales greater than 500,000 MWhrs adopt the smart metering option with time-based rates. Most U.S. states are currently pursuing smart meter deployment and 60 million meters are expected to be in place by For example, San Diego Gas & Electric (SDG&E) replaced its 1.4 million meters with Marisa Zindler, Chip Fox ABB/Ventyx Retail Operation Department {Marisa.Zindler, Chip.Fox}@ventyx.abb.com smart meters by the end of Pacific Gas and Electric (PG&E) has deployed over 4 million smart meters since Southern California Edison plans to install nearly 5 million smart meters to all residential and small business customers by the end of Texas legislators passed a bill in 2005 encouraging smart metering by utilities and passed another bill in 2007 encouraging the rapid deployment of smart meters [2][3]. Furthermore, the deployment of smart meters is taking place not only in the United States but also throughout the world. Based on the current estimation, by 2015, smart meter installations worldwide are expected to reach 250 million. With the aggressive deployment of AMI in distribution utilities, the meter data management system (MDMS) has become a critical gateway to unleash the potentials of AMI. Functionally, MDMS can simplify the integration of AMI systems and facilitate the distribution of the meter data across the utility enterprise by framing the volumes of field data into manageable and understandable information. As the information gateway, the MDMS essentially exchanges the information between various AMI systems and smart grid applications (e.g., collecting meter measurements from AMIs and tailoring the information for various smart grid applications). For instance, with interval residential measurements being accessible by the utility billing and customer information (CIS) system through the AMI + MDMS infrastructure, CIS can now offer better analytic results and flexible pricing schemes based on the time-of-use (TOU) rates. Under this situation, defining a unified information model for MDMS, which seamlessly adapts to the diversification of AMI systems and smart grid applications, becomes necessary. Figure 1 illustrates the diversification existing in AMI systems and smart grid applications: On one hand, the MDMS need to collect data from AMI systems that use different types of information models (e.g., standard or proprietary information models); on the other hand, MDMS tailors the collected meter information to support various smart grid applications that have special requirements on the meter information.

2 on the meter data modeling. Figure 1 Information Flow between AMI and Smart Grid Applications To avoid the information gaps caused by the diversified information models used by AMI systems and smart grid applications, many information standards are proposed to standardize the information contained in both AMI and smart grid applications. The typical standards on the AMI side are ANSI C12.19 (2008) [4], IEC /62 (2006)[5][6]; while the typical standard on the smart grid application side is IEC (2009) [7]. The main goal of this paper is to first investigate the smart meter information standards published by the primary standard institutes (e.g., ANSI, IEC and IEEE) and then propose a unified meter information model that seamlessly supports most AMI systems and smart grid applications in the current and the future based on the investigation. The rest of the paper is structured as follows: Section II studies the primary meter information standards in the current market and summarizes the features of each standard. Section III proposes a unified information model that is maximally compatible with the discussed meter information models and supports various smart gird applications. Section IV briefly discusses the example smart grid applications supported by the unified information model. Section V concludes this paper. II. STANDARD METER INFORMATION MODELS There are several meter information standards in the current power systems market, including ANSI C12.19, IEC /62, IEC61968, and IEEE 1703/D9 [8]. Both ANSI C12.19 and IEC /62 are general meter information models that are applicable to the electricity domain, the gas domain, and the water domain. ANSI C12.19 is widely used in the US market, whereas the IEC /62 primarily concentrates on the European market. As the meter model in the common information model (CIM [9]) the widely accepted information model in the electricity domain, IEC primarily models the behaviors of exchanging meter information between electricity applications. Copublished with ANSI C12.19, the IEEE P1377/D9 (the draft 9 of IEEE P1377) is the alias of ANSI C However, published in 2011, IEEE P1377/D9 reflects the latest progress A. ANSI C12.19 (2008) The ANSI C12.19 is a general meter information model, serving domains of electricity, gas, and water. It is a result from comprehensive cooperative effort among utilities, meter manufacturers, automated meter reading service companies, and many other interested parties, such as the ANSI, Measurement Canada (for Industry Canada), NEMA, and the IEEE Utilimetrics. Currently, ANSI C12.19 has two versions: ANSI C and ANSI C The latter is intended to capture the concepts of the most recent progress on AMI. The heart of ANSI C12.19 is a set of pre-defined tables and procedures. Tables are primarily data structure to store meter measurements and meter metadata, while procedures define methods to operate data in tables. In addition, C12.19 groups the tables that pertain to a particular feature set and a related function together into a decade. In the high level, C12.19 models the meter measurements, meter control, and power quality information. Table 1 highlights the major information modeled by ANSI C Table 1 The Highlighted Information Covered by ANSI C12.19 In addition to modeling the information, C12.19 also defines services to transport tables. The basic services are read and write services. The former causes the transfer of data from the initiating device to the target device and is required for two-way communications. The latter causes the transfer of unrequested data to a target device from the initiating device and is required for both one- and two-way communications. B. IEC /62 (2006) IEC62056, which consists of a series of standards on data exchange for meter reading, tariffs, and load control, defines the meter interface classes for the companion specification of the energy metering (COSEM) model. Among these standards, the IEC and the IEC are related to modeling meter information: the former defines an object identification system (OBIS), while the latter defines interface classes and a data model based on OBIS.

3 IEC specifies the overall structure of the identification system and the mapping of all data items to their identification codes. It provides a unique identifier for all items within the metering equipment, including measurement values, meta data used for configuration and obtaining information about the behavior of the metering equipment. IEC models the meter information through COSEM objects, which are defined by object-oriented methods. The attributes and methods of the COSEM objects can be accessed and used via the messaging services defined in IEC , the application layer of the IEC62056 series standards. Similar to ANSI C12.19, IEC covers the electricity measurements (e.g., accumulated energy usage, current, and voltage), the control information (e.g., the I/O control signals), and the power failure information. C. IEC (2009) IEC defines a standard for the integration of metering systems (e.g., AMI and AMR) with other systems and business functions within the scope of IEC61968 the common information model (CIM) on distribution management system (DMS). Hence, the scope of IEC is to exchange information between a metering system and other systems within the utility enterprise. Unlike ANSI C12.19 and IEC , IEC is a power system specific meter information model, focusing on the meter information specifically used in the power system domain, such as load control, dynamic pricing, outage detection, distribution energy resource (DER) control signals, and onrequest read. D. IEEE P1377/D9 (2011) The IEEE P1377/D9 standard is co-published with ANSI C Publishing C12.19 as IEEE P1377/D9 breaks through the fence of the U.S. market and offers C12.19 an opportunity to go worldwide. E. Discussion Figure 2 illustrates the landscape of exchanging meter information between AMI and smart grid applications [10]- [13], in which ANSI C12.19 and IEC /62 are two major meter information standards used in AMI systems in the current market, and IEC is used to model the meter information exchange behavior between AMI and the distribution management system (DMS). The smart grid applications in the DMS territory (e.g., VVO and Load Forecasting) generally have specific requirements on consuming the meter information. Therefore, the meter information in AMI should be tailored to specific smart grid applications. Figure 2 Exchanging meter information between AMIs and smart grid applications Figure 3 illustrates the meter information covered by ANSI C12.19 and IEC In the high level, both standards include meter metadata (e.g., meter vendor information, background information related with a smart meter), meter measurements (e.g., energy, voltage and current), meter control, and add-on information. The add-on information can be further classified into history & logs and power quality events. Published in 2008, ANSI C12.19 incorporates many newly defined smart grid functionalities: for example, it models control behaviors in details, which include direct control, schedule control, condition control, payment control, and billing control. By contrast, IEC only defines a meter as an open-close switch, the complex control logics are implemented by the high level information models (e.g., IEC ) or application level standards (e.g., IEC [14]). Essentially, ANSI C12.19 and IEC /62 present meter information in different ways: ANSI C12.19 uses tables to store data and defines procedures to operate the data; while IEC /62 strictly follows the object-oriented modeling conventions, modeling information through attributes and methods of an object. Beyond modeling data, C12.19 also standardizes services (e.g., the read service and/or write service) to expose information to the high level applications; by contrast, IEC /62 adopts the assessable object features and methods to expose its information to the high level applications. From the methodology s aspect, the objectbased IEC /62 meter model is more straightforward than the table-based ANSI C12.19 model. Considering the above gaps in semantics and the ways to expose information between different standards, practically extracting required information from different standards adopted by AMI systems becomes challenge. Under this situation, unifying the information gaps and the way of exposing data becomes important. III. A UNIFIED METER INFORMATION MODEL (UMIM)

4 Based on the investigation of the general meter information standards in state of the art, we propose a unified meter information model that is maximally compatible with the discussed AMI meter information models, meanwhile supporting the customer billing system and other potential smart grid applications. measurement, used to distinguish from history measurements, refers to the measurements in the current timeframe. (6) The history measurements entity has three subtypes: electricity raw data history, water raw data history, and gas raw data history, to handle meter data from electricity, water, Figure 3 The information covered by ANSI C12.19 and IEC The preliminary high level entity relationship (ER) model is proposed in Figure 4. Due to the space limit, only important attributes are included in this preliminary high level ER diagram. The extensible entities and relationships that address potential requirements in the future are in gray. The rationales of designing the above ER model are listed below: (1) A utility has end devices. A meter manufacturer has end devices. An end device has status. (2) The End Device entity has three subtypes: electricity device, water device, and gas device, separately handling meter data from electricity, water, and gas utilities. (3) A number of end devices are grouped into one end device group. Each end device group has a set of TOU configuration to implement dynamic pricing. (4) An end device generates raw measurements that include: staging measurements and history measurements. After receiving the raw meter data from AMI, the MDMS performs VEE (validation, estimation, and editing), and loads cleaned data into a staging measurements table. The cleaned data are further aggregated per requirements from applications and stored to the aggregated measurements tables. Once the aggregation process is finished, the data in the staging measurements table are periodically move to the history table for further use. (5) The staging measurements entity has three subtypes: staging electricity measurements, staging water measurements, and staging gas measurements, to handle meter data from electricity, water, and gas utilities. The staging and gas utilities. (7) The aggregated measurements entity has one or more subtypes to support various smart grid applications. (8) The entity external system integration info is an interface to integrate the information extracted from external systems. IV. APPLICATIONS With the deployment of AMI, integrating meter information provided by AMI with DMS application becomes important. This section illustrates three smart grid applications supported by the proposed unified meter information model: demand response (DR), distribution state estimation (DSE), and outage management system (OMS). A. Demand Response Demand response (DR) refers to the capability of a smart grid to allow end-use of resources such as power, water, gas, etc. by customers to reduce their usage in a given time period, or shift that usage to another time period, in response to a price signal, a financial incentive, an environmental condition, or a reliability signal [2], [14]. Demand Response saves ratepayers money by lowering peak time resources usage, which are high-priced. This lowers the price of wholesale resources, and in turn, retail rates. Demand Response may also prevent rolling blackouts by offsetting the need for more resources generation and can mitigate the resources generator market. New advanced metering technology on the market today provides a better solution for residential Demand Response programs. These single-phase solid-state meters can measure, calculate and store data within the meter and also

5 transmit it to central repositories. These meters are capable of calculating daily load usage, demand, time-of-use (TOU), and critical-tier usage data. This data is available to both the consumer and service provider locally at the meter via the reprogramming or meter change-outs required (d) load profiling of selected meter locations (e) tamper indications and alarms (f) remote connect and disconnect in the meter Figure 4 The Unified Meter Information Model meter display. The data can also be transmitted to remote metering automation or collection systems. Even though these meters store and transmit billing data, they can also record interval data for load studies, but interval data is not needed for the acquisition of daily energy usage and complex forms of billing data. These advanced meters give utilities choices in how they set up their Demand Response programs. Meters can be programmed to operate as: (a) TOU meters with blocks of time (b) meters that measure time-differentiated usage (c) load profile meters (d) meters with dynamic TOU that have defined blocks of time that can be changed daily The advanced metering systems have additional functionality such as: (a) capability of being scheduled and having on-request remote meter reading services for billing or other data needs (b) advanced energy measurement options, including energy in (delivered), energy out (received), sum, and net metering (c) implementation of consumption, demand, TOU and critical-tier pricing rates, with no on-site visits for (g) demand limiting in the meter Meters with this advanced metering or smart metering technology can be deployed on metering automation systems and they need to have a unified information standard model as described in this paper. These systems are designed to meet the needs of utilities in both regulated and deregulated markets and are suitable for either large-scale deployments or targeted applications. B. Distribution state estimation DSE aims to provide the timely and accurate monitoring of the distribution system operating condition, which serves as a solid foundation for various other smart grid technologies, such as voltage and var control, feeder reconfiguration, and post-disturbance service restoration. In the past several years, distribution utilities have been installing and/or upgrading the monitoring and control systems that typically include supervisory control and data acquisition (SCADA) systems and advanced metering infrastructures (AMI). The SCADA and AMI systems offer redundant real-time or near real-time measurements, among which residential meter data constitute the majority. However,

6 hindered by deficiencies of the measurement facilities and/or errors in the data transmission systems (i.e., lost connection during data transport, data corruption, etc), the large amount of measurements inevitably contains inaccurate and unreliable data, requiring DSE screen out bad data and estimate true values of the system states. In practice, the DSE can be performed in every 5~15 minutes and requires regularly collecting meter measurements from millions of residential smart meters with the same frequency. The meter measurement information used in the DSE application generally includes the real and reactive power values of each load, which can be covered by the instantaneous measurements of the unified meter model. C. Outage Management System After a fault occurs in the power distribution system, before dispatching crew to the field for fault repairing and service restoration to the affected customers, the outage root (operated protective device or the open conductor) and the outage area boundary need to be identified first. This process is called the outage scoping analysis (OSA) and is the fundamental function in outage management system (OMS). Traditionally, the main outage information source for conducting the OSA are customer trouble calls, i.e., some customers in the outage area who experience the loss of power supply may call the utility to report the power outage. The trouble call-based outage scoping usually results in prolonged OSA procedure due to the possible time latency caused by customers absence (for instance when most customers are at work or asleep). In addition, the accuracy of the results of the outage scoping analysis is directly impacted by the number of trouble calls received. In reality, eventual outage confirmation relies on sending the crew to the field, which could take hours. For the distribution network equipped with AMI, the information of last gasp, which primary includes the off status of switch in the smart meters and its timestamp, sent by smart meters before they lose voltage during an outage is an effective outage notification. The meter last gasp comes earlier than customer trouble calls, carrying more precise outage information. In addition, the communication network of the AMI system also enables the on-demand polling of meter status for the purpose of outage confirmation. In summary, the DR, DSE and OMS applications requires different types of smart meter information, DR needs the TOU and meter control information, DSE requires the regularly meter measurements on real and reactive power of the load, while OMS requires the meter last gasp information. Besides the discussed application, the implementation of many other smart grid applications such as voltage var control is also based on smart meter information. The unified meter information model will facilitate the information exchange between the smart grid applications and AMI systems. V. CONCLUSIONS With the development of Smart Grid and the wide deployment of AMI systems, MDMS becomes the information gateway of the utility enterprise applications, aggregating the meter information from different AMI systems and tailoring to support various smart grid applications. However, practically diversified meter information models used by AMI products and smart grid applications complicate the information exchange between different systems. To ease the complexity of exchanging information between different systems and maximally bridge the information gaps between various AMI and smart grid applications, this paper proposes a unified information model for MDMS. Furthermore, the effectiveness of the proposed unified information model has been discussed against three widely applied smart grid applications, including DR, DSE and OMS. ACKNOWLEDGEMENT Financial support from the ABB Corp. Research Center is gratefully acknowledged. The authors appreciate the discussions and help from colleagues Kevin Burandt, Samantha Hines. REFERENCES [1] Energy Policy Act of 2005, 109publ58/pdf/PLAW-109publ58.pdf [2] S. Borenstein, M. Haske, A. 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