978-1-4799-5141-3/14/$31.00 ©2014 IEEE

Advanced Metering Infrastructure Analytics -A Case Study

I S Jha Director (Projects)

PGCIL, Gurgaon, India

Subir Sen GM (Smart Grid & EE) PGCIL, Gurgaon, India

Vineeta Agarwal DGM (Smart Grid)

PGCIL, Gurgaon, India

Abstract— Advanced Metering Infrastructure

(AMI) is the basic building block for development of Smart Grid in Distribution System. The main purpose of AMI is to enable two way communication between consumer and Smart Grid Control Center of Utility which involves remote monitoring & control of energy consumption as well as other parameters in real time. Meter data analytics play a vital role in AMI system which helps utility to manage their resources and business process efficiently. Indigenously developed meter data analytics such as meter data validation, energy audit & accounting of distribution transformer, missing information, peak demand identification, consumer profile analysis, load forecasting, abnormal energy pattern analysis etc. which helps utilities through improved visualization and enhanced situational awareness. These would also help in providing better QoS to consumers as well as empower them for better energy management. This paper presents several analytics developed on smart meter data as part of AMI implemented in Puducherry Smart Grid Pilot Project.

Keywords—Advanced Metering Infrastructure, Data Concentrator Unit, Distribution Transformer, Meter Data Acquisition System, Meter Data Management System

I. INTRODUCTION

The electricity sector is confronted by critical challenges viz. growing energy demand, high AT&C losses, concern on reliability & quality of power supply, fuel constraints and implementation of environmental policies to combat climate change etc. [1]. These challenges are leading to recognition of consumers and utility as smart energy decision-makers and advancement of energy efficiency in real time. In this direction, Smart Grid technologies bring

efficiency and sustainability in meeting the growing electricity demand with reliability and best of the quality. However, Smart Grid is applicable to all value chain of power system but in distribution it plays a vital role.

Advanced Metering Infrastructure (AMI) is the basic building block of Smart Grid. It is defined as a system that measure, collect, transfer and analyze energy usage and communicate with metering devices. It enables end users to participate in reducing peak demands and in contributing to energy management process. Further, meters can also capture, receive and execute remote commands like load disconnect/connect.

The main enabling features of an AMI infrastructure include smart meter, communication medium, MDAS/MDM, load monitoring, Demand response, Load control, Tamper detection, Alarm handling, Real time energy audit, Time of Day (ToD) tariff etc.

Smart energy meter serves as a gateway between utility and consumer. Although the basic purpose of meter is for energy & other parameters measurement, however smart meters generates lots of data which enable higher resolution for entire electricity delivery system. By capturing smart meter data and converting into actionable point will improve efficiency of distribution utility and provide quality of power to consumer. Smart meter data has an important role in several Smart Grid applications and enables novel data analytics tasks, such as energy consumption behavior, tamper detection, outage management, automated demand response

and energy feedback. It also empowers consumers for better energy management.

Utilities across North America, Europe, Africa and Asia have implemented Advanced metering infrastructure as a cost effective way to modernize their distribution system while enabling consumer participation in energy management [2]. Various analytics such as energy consumption pattern demand response, tamper detection etc. have benefitted these utilities in cutting non-technical losses, supporting network optimization and controlling energy consumption.

This paper presents several analytics developed indigenously on smart meter data as part of Advanced Metering Infrastructure (AMI) implemented in Puducherry Smart Grid Pilot Project. The aim of the paper is to analyse the characteristics of data and to provide utility with actionable information.

II. AMI ARCHITECTURE

In an established AMI system, it is essential to have a common platform for monitoring as well as utilization of essential features of AMI Systems.

The key components of AMI are:

A. Smart Energy Meter Smart Energy Meters act as a source of

information for consumer behaviour pattern, tamper and load control etc. It comprises of memory to store information and communication module to transfer this information to Smart Grid Control Center.

B. Data Concentrator Unit The data from cluster of smart meters are

aggregated by a data concentrator unit (DCU) and then send to the Smart Grid Control Centre. It also sends messages /signals received from the utility / consumer for a particular/all meters to the intended recipient.

C. Smart Grid Control Centre Meter Data Acquisition System (MDAS) and

Servers are located at Smart Grid Control Centre to perform periodic collection of information from smart meters. Logics and validation rules are defined

in Meter Data Management System (MDM) to sanitize the data.

MDAS is a server based meter data head end system compatible with multiple standard based protocols as well as proprietary protocols. MDAS exchange meter data to meter data management systems coupled with analytics on standard data exchange model. A typical architecture of the Advanced Metering System is shown in Fig 1.

Fig. 1. Advanced Metering Infrastructure

III. AMI ANALYTICS: A CASE STUDY

POWERGRID has under taken a pioneering initiative to develop Smart Grid pilot through open collaboration at Puducherry. Different manufactures have provided meters which works on different communication technology. Data from all meters are integrated, synthesized and stored for data analysis and real time monitoring [3].

Smart Meters have been installed at consumer premises including various distribution transformers and feeders. These meters work on various technologies namely PLC, GPRS, RF 2.4 GHz and RF 865 MHz. Smart meters working on RF/PLC communicate to Data Concentrator Unit which transmits the data to meter data acquisition system. However, Smart Meters working on GPRS communicate to MDAS directly. MDAS exchange meter data to meter data management system [4]. All of these components of AMI are integrated at one common platform at Smart Grid Control Center at Puducherry. Virtualized environment at blade server along with storage is setup at control center for monitoring real time energy consumption pattern, other parameters and various alarms associated with

it. The utility can use alarm information for reliability evaluation and failure analysis. Further, information also enables utility to monitor the health/availability of devices in AMI infrastructure. A typical real time availability status of the field devices such as Smart Meters and DCUs available through MDAS are shown in Fig. 2

Fig. 2. Status of Field Devices: Smart Meter and DCU

Typical alarms and alerts such as Load Through Earth Stop Event, Load Reversal Stop Event etc. are observed as shown in Fig 3.

Fig. 3. Alarms for Meter failure & fault

Data collected at MDAS is used to developed meter data analytics to identify the exception and generate lead for carrying out corrective action. Data analytics helps utilities to perform on-line energy audit to operate in efficient manner as well as for better asset management and system planning [5].

The analytics help utility to extract and use the information embedded in meter data which provides many information such as:

• Meter Data Validation.

• Tamper and missing information due to communication failure, meter fault etc.

• Energy Audit & Accounting of Distribution transformer.

• Peak Demand Identification.

• Consumer profile analysis.

• Forecast and build predictive models for demand management or demand response program planning.

• Consumer abnormal pattern analysis.

The above analysis has been done on meter data collected under Puducherry Smart Grid Pilot Project. The data is captured hourly for carrying out analysis. Following analytics are carried out with meter data as described below.

A. Data Validation Abnormal and Missing data input creates big

hassle in analysis [6]. So, very first step of analysis requires fine grained estimation and validation. Data validation identifies parameters that can go wrong at the meter/recorder and cause the data collected not to reflect actual usage. These rules applied to kWh, kVARh, voltage, current, Pf data. It evaluates the quality of the data and generates estimate where errors, overlaps, redundancies and gaps exist. It estimates interval data based on meter readings by filling and correcting the missing gaps and errors. Fig. 4 shows the missed read of some of the meters on a particular day.

Fig. 4. Missed Read on a Patricular day

B. Tamper and Missing Information Meter events and usage information can help in

understanding an overall picture of what’s happening with a consumer’s energy usage over time. This unified view enables to detect energy theft, meter tampering or equipment problems that may be affecting service levels. As availability of supply at consumer end is very crucial, total power off hours during a day and long outage events was analyzed to find out time taken to attend the events or working efficiency of maintenance crew.

Fig. 5 clearly shows consumer meter outage between 14 and 19 hrs. These analytics also help utilities to calculate various reliability indices of distribution system such as MTTR, SAIFI, SAIDI etc.

Fig. 5. Outage Duration

C. Energy Audit & Accounting The efficiency of a power system is determined by the losses involved in the system. All the technical losses and commercial losses include AT & C losses, energy theft etc. which needs to be effectively reduced. To calculate loss at the Feeder level or distribution transformer level energy audit analysis at daily, weekly, monthly was carried out as shown in Fig. 6.

To understand the consumption pattern and to analyze the abnormal consumption of energy by individual consumers various analysis like average percentage loss, maximum, minimum demand of day in DT was carried out. These essential details revealed the nature of load and also helped in forecasting of load.

Fig. 6. A Typical Energy Audit (Intra-Day)

D. Peak Demand For electricity grid, the critical requirement is to

flatten the load curve by peak-clipping and valley-filling through tariff incentive/disincentive or through demand-side management. The advantages of flattening the load curves are cost saving on account of additional infrastructure, energy savings due to reduce grid losses etc.

Aggregate hourly load profiles of any distribution transformer reveal load peaks and valleys in a day. Aggregated meter reading of individual consumer under the same distribution transformer gives the information of consumer contributing in peak and valley. The minimum, maximum, standard deviation can help utility identify the consumers who are drawing more or less during peak hours.

Fig. 7. Peak and Off-Peak Patterns

This analysis has been carried out for different category of consumers to better understand the consumption pattern of residential, commercial and

industrial consumers. These patterns enable utility to design Time of Day (ToD) tariff. Fig. 7 shows the peak and off-peak analysis of distribution transformer. Table 1 shows number of consumer category wise contributing in peak in a distribution transformer.

TABLE 1. NO. OF PARTICIPATING CONSUMERS CATEGORY WISE DURING PEAK

Peak Time (hr) Residential Commercial Industrial

[7,8,9,10] 292 15 13

[18,19,20] 216 12 10

E. Consumer Profile Analytics Daily energy consumption pattern yields

information such as minimum, average and maximum energy consumption as well as change in daily energy usage. These information help consumers in better energy management. A typical consumption pattern of a consumer on a particular day collected through smart meter is shown below in Fig. 8.

Fig. 8. Consumer Profile on a particular day

Further, weekly & monthly analysis of consumer consumption behavior has helped utilities to identify the consumption pattern in working days and holidays and accordingly plan energy requirement for working days & holidays. Fig. 9 shows the consumption pattern of consumer during weekends.

Fig. 9. Consumption Pattern during Weekends

F. Load Forecasting Load Forecasting is essential for defining the

requirements of the distribution network capacity, scheduling, approximating AT&C losses, estimating the existing networks capability to transfer increasing loads and create effective demand response programs. On the basis of previous energy pattern of usage, newly pattern can be identified.

G. Abnormal Behavior Identification By analyzing consumption pattern of consumers,

utilities are able to distinguish between a normal daily consumption patterns from an abnormal one. Based on historical data of consumer, the utility can identify irregular consumption and detect potential issues. Therefore, daily energy consumption patterns can be an important variable to monitor and trigger consumer action.

Fig. 10. Consumer Profile of particular day

Fig. 10 shows the weekly consumer profile of a particular consumer with different color showing each day in a week. From the graph, it is evident that

unusual consumption took place on Friday i.e., on others days consumer behaviour was following average consumption pattern but on Friday its consumption increased rapidly.

IV. CONCLUSION

Smart Grid technologies have presented new dimensions in entire electricity delivery system by providing higher resolution for real time operation as well as by empowering consumer to take control of their electricity usages. Analytics of meter data extracted in different time horizon with different time resolution is helpful in planning and meeting energy requirements. Meter Data Analytics will help utilities to identify challenges in the existing distribution system and operate in an efficient manner by enabling energy audit. These analytics integrated with Management Information System (MIS) gives insight on evaluating network performance, growing demand, quality of power supply etc. to support decision making process. It enables consumers to view their own consumption behaviour, which facilities them to control their energy usage and optimize energy bills. It also provides an opportunity for consumers to participate in demand response program and manage the available resources efficiently.

ACKNOWLEDGEMENT

Authors are thankful to the management of POWERGRID for granting permission for presentation of this paper. Views expressed in the paper are of the authors only and need not necessarily be that of the organization in which they belong.

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