Power distribution companies have made significant investments in the deployment of information technology (IT) systems with the objective of reducing aggregate technical and commercial losses. However, the lack of transparency in operations persists due to the limited penetration and convergence of IT and operation technologies. System integration through the deployment of technology solutions such as meter data acquisition systems (MDAS) can play a crucial role in empowering discoms. The deployment of MDAS, as well as meter data management (MDM) solutions, has become increasingly significant with smart metering gaining popularity among utilities. The use of MDAS is critical for realising the full potential of advanced metering infrastructure (AMI).
Functioning of MDAS
Generating consumption and billing data through meters is a precursor to MDAS. Once the data is generated, the acquisition process involves three components – data collection, data transmission and data preprocessing. Data collection involves gathering instantaneous data and storing it in the form of logs and meter events. The acquired data goes through sensing and metering processes, which facilitate real-time data integration. The data transmission process comprises inter- and intra-device data transmission. Inter-device data transmission involves the transfer of data from the meter to the display and control unit (DCU) while intra-device transmission involves DCU-to-DCU transmission.
The final step is data analysis or preprocessing, which involves transforming raw data into an understandable format. It entails the comparison of historical data with newly collected real-time data through data modelling and the use of analytics to draw insights. Under preprocessing, the data redundancy and consistency process allows the correction of errors in the transmitted data in certain permissible ways. The data undergoes integration and validation, and is stored in a proper format for querying and analysis. The final step is the dissemination of the analysed information through dashboards and reports to assist in efficient decision-making.
MDAS and AMI
MDAS is the technology component in the AMI landscape. It collects data by deploying smart meters, along with an appropriate communication network such as general packet radio service (GPRS) and Wi-Fi. With an interoperable head-end system in place, which can communicate with different mixes of meters, AMI data is retrieved through various control commands. Through MDM, the retrieved data is stored in an AMI data warehouse. MDM also consists of inherent features related to validation, estimation and editing, based on the requirement of the particular industry, and hence leads to data standardisation. MDAS enables AMI integration with enterprise-wide systems and acts as an interface for various applications within the utility. Ultimately, MDAS establishes certain custom applications for data analysis, forecasting and trend analysis, and communicates the results to department heads, consumers and regulators.
Benefits of MDAS
The critical role of MDAS relates to the collection of large volumes of interval meter data very quickly. It facilitates meter integrity for utilities by managing the collection of meter readings and enables requests from multiple technologies for the same meter. This allows merged utilities to adopt new technologies and unify billing processes without completely re-engineering the existing meter reading processes. MDAS also allows utilities to have enhanced network visibility and, in turn, improve their demand-supply planning. Since MDAS allows the framing of volumes of interval data into manageable and familiar information, it helps utilities move beyond billing information and focus on utility management. It also enables the deployment of advanced analytics solutions such supervisory control and data acquisition systems, statistical analysis systems and big data, which provide flexibility for MDAS solutions.
With two-way communication, MDAS provides access to information on the consumption pattern of consumers and also helps settle customer bill disputes. It enables energy efficiency at the consumers’ end by allowing them to access demand-side management applications. Time-of-day billing under MDAS allows customers to control their consumption during off-peak hours and hence reduce their utility bill. Customer service calls also decline since the transactions can be viewed online.
In terms of the environmental aspect, the implementation of MDAS leads to lower consumption and losses, which, in turn, helps to reduce carbon emissions. It also complies with regulatory and policy requirements related to power quality and conservation.
Various technology options are available for MDAS. However, while choosing between these options, their compatibility with the existing technology landscape of the utility should be taken into consideration. Other applications should be able to leverage the same software without any major custom design requirements. The techno-logy solution should be scalable, that is, it should be capable of accommodating increased growth as per the industry type. The communication range and the ability to reach the end-consumer’s meter are critically important. Any new technology option should take into account the different geographic conditions in cities as well as in villages. Spectrum availability is another key issue in the Indian context. The data should be insensitive to external interference and should be encrypted to avoid any losses.
The solution option would involve an interoperable head-end system, followed by a meter data management system. Some of the solution providers are Oracle, Itron and Aclara. The technology solution should also be supported by various AMI advanced applications like executive dashboard, revenue protection system, outage management system and forecasting system.
Use of analytics
A large amount of a utility’s bandwidth is utilised to manage MDAS because of the large amount of data that it has collected and transmitted. This often restricts the utility from performing its core business functions. In such a scenario, a utility should deploy a cloud-based computing solution and employ big data analytics. This allows for interoperability at the device- and head-end levels, thereby providing the freedom to choose meter suppliers. The major advantage of this approach is that the same platform can be leveraged to develop key applications such as outage management and revenue protection systems, among others.
Big data analytics offer valuable insights to integrate, store, analyse and predict future events based on real-time event correlation and available historical data. It also enables the creation of an executive dashboard, which provides role-based real-time key performance parameters. It is capable of transforming the existing outage management system into a smart outage management and restoration system. It also improves grid/substation performance management and tracking, as well as detects pilferage and enables the efficient management of real-time power quality issues.
The growing number of installed meters and increasing data volume pose a serious challenge to MDAS. Utilities face major difficulties in capturing data and transferring it to the data centre remotely through GPRS-based communication systems. The inability to capture field data at the right time leads to underutilisation. The difficulties of storing data and ensuring device protection are also limiting the scope of MDAS. Besides these issues, static and dynamic data variations, data inconsistency and missing data pose additional challenges to the functioning of MDAS and hinder future scalability.
There are a number of solutions and practices that can be followed to deal with the challenges related to MDAS optimisation. Managing the large volume of meter data generated by AMI systems necessitates the implementation of consistent, corporate-wide best practice rules. There is a need to recognise MDAS as not merely a billing tool but also as a strategic tool to improve customer service. Time synchronisation for collecting data is crucial to its success. There is a need to simplify the interfacing of MDAS with other IT systems in the utility.
The development of standardised specifications for interoperability will simplify the usage of MDAS. MDAS should also be made capable of handling the storage and distribution of non-billing data as well as of managing tamper alarms and demand-response events. It is imperative to ensure the highest meter data security standards in order to enable competition among various players. Finally, emphasis should be laid on moving towards cloud-based solutions using the big data analytics platform with mobility support.
Based on presentations by R. Rammohan, Business Head, EMS, CMS Computers; and Jasdev Soni, Principal Consultant, Energy and Utilities, Infosys at a recent Power Line conference